blob_id stringlengths 40 40 | bodies listlengths 2 6 | bodies_text stringlengths 196 7.73k | class_docstring stringlengths 0 700 | class_name stringlengths 1 86 | detected_licenses listlengths 0 45 | format_version stringclasses 1
value | full_text stringlengths 467 8.64k | id stringlengths 40 40 | length_bytes int64 515 49.7k | license_type stringclasses 2
values | methods listlengths 2 6 | n_methods int64 2 6 | original_id stringlengths 38 40 ⌀ | prompt stringlengths 160 3.93k | prompted_full_text stringlengths 681 10.7k | revision_id stringlengths 40 40 | skeleton stringlengths 162 4.09k | snapshot_name stringclasses 1
value | snapshot_source_dir stringclasses 1
value | solution stringlengths 331 8.3k | source stringclasses 1
value | source_path stringlengths 5 177 | source_repo stringlengths 6 88 | split stringclasses 1
value | star_events_count int64 0 209k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
4964eb35309f007839db58a757a265390428f46c | [
"if compile:\n regex = re.compile(regex)\nmm = regex.findall(line)\nif mm:\n return mm\nelse:\n return 0",
"if compile:\n regex = re.compile(regex)\nmm = regex.match(line)\nif mm != None:\n return mm.groups()\nelse:\n return 0",
"if compile:\n regex = re.compile(regex)\nline = regex.sub(s, ... | <|body_start_0|>
if compile:
regex = re.compile(regex)
mm = regex.findall(line)
if mm:
return mm
else:
return 0
<|end_body_0|>
<|body_start_1|>
if compile:
regex = re.compile(regex)
mm = regex.match(line)
if mm != N... | regular expression functions | Lib | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Lib:
"""regular expression functions"""
def FindAll(self, line, regex, compile=0):
"""find all regex 'regex' in string 'line' compile: must we compile a regex before, or is it compiled?"""
<|body_0|>
def Match(self, line, regex, compile=0):
"""find regex 'regex' ... | stack_v2_sparse_classes_10k_train_005200 | 1,115 | no_license | [
{
"docstring": "find all regex 'regex' in string 'line' compile: must we compile a regex before, or is it compiled?",
"name": "FindAll",
"signature": "def FindAll(self, line, regex, compile=0)"
},
{
"docstring": "find regex 'regex' in string 'line' compile: must we compile a regex before, or is ... | 3 | stack_v2_sparse_classes_30k_train_003264 | Implement the Python class `Lib` described below.
Class description:
regular expression functions
Method signatures and docstrings:
- def FindAll(self, line, regex, compile=0): find all regex 'regex' in string 'line' compile: must we compile a regex before, or is it compiled?
- def Match(self, line, regex, compile=0)... | Implement the Python class `Lib` described below.
Class description:
regular expression functions
Method signatures and docstrings:
- def FindAll(self, line, regex, compile=0): find all regex 'regex' in string 'line' compile: must we compile a regex before, or is it compiled?
- def Match(self, line, regex, compile=0)... | 3cfcae894c165189cc3ff61e27ca284f09e87871 | <|skeleton|>
class Lib:
"""regular expression functions"""
def FindAll(self, line, regex, compile=0):
"""find all regex 'regex' in string 'line' compile: must we compile a regex before, or is it compiled?"""
<|body_0|>
def Match(self, line, regex, compile=0):
"""find regex 'regex' ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Lib:
"""regular expression functions"""
def FindAll(self, line, regex, compile=0):
"""find all regex 'regex' in string 'line' compile: must we compile a regex before, or is it compiled?"""
if compile:
regex = re.compile(regex)
mm = regex.findall(line)
if mm:
... | the_stack_v2_python_sparse | dmerce2/Core/RegExp.py | rbe/dmerce | train | 0 |
9ea1c2746cd676d8a16df87e9b920ae9f6c52dd6 | [
"top_n = 2\nseq_length = 4\nxlnet_base = _get_xlnet_base()\nxlnet_trainer_model = xlnet.XLNetSpanLabeler(network=xlnet_base, start_n_top=top_n, end_n_top=top_n, initializer=tf.keras.initializers.RandomNormal(stddev=0.1), span_labeling_activation='tanh', dropout_rate=0.1)\ninputs = dict(input_word_ids=tf.keras.layer... | <|body_start_0|>
top_n = 2
seq_length = 4
xlnet_base = _get_xlnet_base()
xlnet_trainer_model = xlnet.XLNetSpanLabeler(network=xlnet_base, start_n_top=top_n, end_n_top=top_n, initializer=tf.keras.initializers.RandomNormal(stddev=0.1), span_labeling_activation='tanh', dropout_rate=0.1)
... | XLNetSpanLabelerTest | [
"Apache-2.0",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class XLNetSpanLabelerTest:
def test_xlnet_trainer(self):
"""Validate that the Keras object can be created."""
<|body_0|>
def test_serialize_deserialize(self):
"""Validates that the XLNet trainer can be serialized and deserialized."""
<|body_1|>
<|end_skeleton|>
... | stack_v2_sparse_classes_10k_train_005201 | 13,124 | permissive | [
{
"docstring": "Validate that the Keras object can be created.",
"name": "test_xlnet_trainer",
"signature": "def test_xlnet_trainer(self)"
},
{
"docstring": "Validates that the XLNet trainer can be serialized and deserialized.",
"name": "test_serialize_deserialize",
"signature": "def tes... | 2 | stack_v2_sparse_classes_30k_train_000696 | Implement the Python class `XLNetSpanLabelerTest` described below.
Class description:
Implement the XLNetSpanLabelerTest class.
Method signatures and docstrings:
- def test_xlnet_trainer(self): Validate that the Keras object can be created.
- def test_serialize_deserialize(self): Validates that the XLNet trainer can ... | Implement the Python class `XLNetSpanLabelerTest` described below.
Class description:
Implement the XLNetSpanLabelerTest class.
Method signatures and docstrings:
- def test_xlnet_trainer(self): Validate that the Keras object can be created.
- def test_serialize_deserialize(self): Validates that the XLNet trainer can ... | 6fc53292b1d3ce3c0340ce724c2c11c77e663d27 | <|skeleton|>
class XLNetSpanLabelerTest:
def test_xlnet_trainer(self):
"""Validate that the Keras object can be created."""
<|body_0|>
def test_serialize_deserialize(self):
"""Validates that the XLNet trainer can be serialized and deserialized."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class XLNetSpanLabelerTest:
def test_xlnet_trainer(self):
"""Validate that the Keras object can be created."""
top_n = 2
seq_length = 4
xlnet_base = _get_xlnet_base()
xlnet_trainer_model = xlnet.XLNetSpanLabeler(network=xlnet_base, start_n_top=top_n, end_n_top=top_n, initiali... | the_stack_v2_python_sparse | models/official/nlp/modeling/models/xlnet_test.py | aboerzel/German_License_Plate_Recognition | train | 34 | |
8a7959652c3ad690e8f5536089b278135343e145 | [
"dp = [0] * (n + 1)\ndp[0], dp[1] = (1, 1)\nfor i in range(2, n + 1):\n for j in range(i):\n dp[i] += dp[j] * dp[i - j - 1]\nreturn dp[n]",
"dp = [[False for i in range(n)] for j in range(n)]\n\ndef dfs(left, right, x):\n if dp[len(left)][len(right)] != False:\n return dp[len(left)][len(right)... | <|body_start_0|>
dp = [0] * (n + 1)
dp[0], dp[1] = (1, 1)
for i in range(2, n + 1):
for j in range(i):
dp[i] += dp[j] * dp[i - j - 1]
return dp[n]
<|end_body_0|>
<|body_start_1|>
dp = [[False for i in range(n)] for j in range(n)]
def dfs(left... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def numTrees(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def numTrees_rec(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
dp = [0] * (n + 1)
dp[0], dp[1] = (1, 1)
for i in range... | stack_v2_sparse_classes_10k_train_005202 | 1,585 | no_license | [
{
"docstring": ":type n: int :rtype: int",
"name": "numTrees",
"signature": "def numTrees(self, n)"
},
{
"docstring": ":type n: int :rtype: int",
"name": "numTrees_rec",
"signature": "def numTrees_rec(self, n)"
}
] | 2 | stack_v2_sparse_classes_30k_train_005684 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def numTrees(self, n): :type n: int :rtype: int
- def numTrees_rec(self, n): :type n: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def numTrees(self, n): :type n: int :rtype: int
- def numTrees_rec(self, n): :type n: int :rtype: int
<|skeleton|>
class Solution:
def numTrees(self, n):
""":type n... | ed0837ce14a22660657ffd15ff99d7cb1804e8c1 | <|skeleton|>
class Solution:
def numTrees(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def numTrees_rec(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def numTrees(self, n):
""":type n: int :rtype: int"""
dp = [0] * (n + 1)
dp[0], dp[1] = (1, 1)
for i in range(2, n + 1):
for j in range(i):
dp[i] += dp[j] * dp[i - j - 1]
return dp[n]
def numTrees_rec(self, n):
""":type... | the_stack_v2_python_sparse | python/096-unique-binary-search-trees.py | ByronHsu/leetcode | train | 5 | |
39a350c094dc6dfa2cc94d925aedccaa2cea19e7 | [
"quadrado = Quadrado()\nquadrado.lado = 4\nself.assertEquals(4, quadrado.RetornaLado())",
"quadrado = Quadrado()\nquadrado.lado = 3\nself.assertEquals(9, quadrado.CalcularArea())"
] | <|body_start_0|>
quadrado = Quadrado()
quadrado.lado = 4
self.assertEquals(4, quadrado.RetornaLado())
<|end_body_0|>
<|body_start_1|>
quadrado = Quadrado()
quadrado.lado = 3
self.assertEquals(9, quadrado.CalcularArea())
<|end_body_1|>
| MyQuadradoTest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MyQuadradoTest:
def testRetornaLado(self):
"""Função que testa o método 'Retorna Lado' da classe Quadrado :return: sem retornos."""
<|body_0|>
def testeCalcularArea(self):
"""Função que testa o método 'CalculaArea' da classe Quadrado :return: sem retornos."""
... | stack_v2_sparse_classes_10k_train_005203 | 784 | no_license | [
{
"docstring": "Função que testa o método 'Retorna Lado' da classe Quadrado :return: sem retornos.",
"name": "testRetornaLado",
"signature": "def testRetornaLado(self)"
},
{
"docstring": "Função que testa o método 'CalculaArea' da classe Quadrado :return: sem retornos.",
"name": "testeCalcul... | 2 | stack_v2_sparse_classes_30k_train_003270 | Implement the Python class `MyQuadradoTest` described below.
Class description:
Implement the MyQuadradoTest class.
Method signatures and docstrings:
- def testRetornaLado(self): Função que testa o método 'Retorna Lado' da classe Quadrado :return: sem retornos.
- def testeCalcularArea(self): Função que testa o método... | Implement the Python class `MyQuadradoTest` described below.
Class description:
Implement the MyQuadradoTest class.
Method signatures and docstrings:
- def testRetornaLado(self): Função que testa o método 'Retorna Lado' da classe Quadrado :return: sem retornos.
- def testeCalcularArea(self): Função que testa o método... | 0ebcb2da872fcd5c101826455710634a3e6e69cb | <|skeleton|>
class MyQuadradoTest:
def testRetornaLado(self):
"""Função que testa o método 'Retorna Lado' da classe Quadrado :return: sem retornos."""
<|body_0|>
def testeCalcularArea(self):
"""Função que testa o método 'CalculaArea' da classe Quadrado :return: sem retornos."""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MyQuadradoTest:
def testRetornaLado(self):
"""Função que testa o método 'Retorna Lado' da classe Quadrado :return: sem retornos."""
quadrado = Quadrado()
quadrado.lado = 4
self.assertEquals(4, quadrado.RetornaLado())
def testeCalcularArea(self):
"""Função que testa... | the_stack_v2_python_sparse | Projeto_Testes/First_TDD_File.py | gnfandrade/Projetos_Python | train | 0 | |
69cd7a004977df6951dda67690458c86b1397761 | [
"if isinstance(expressions, tuple):\n expressions = [expressions]\nmasks = [list(comp(self.loc[:, method], thr)) for method, comp, thr in expressions]\nif len(masks) > 1:\n masks = numpy.logical_and(*masks)\nelse:\n masks = masks[0]\nreturn TAPPredictionResult(self.loc[masks, :])",
"if type(others) == ty... | <|body_start_0|>
if isinstance(expressions, tuple):
expressions = [expressions]
masks = [list(comp(self.loc[:, method], thr)) for method, comp, thr in expressions]
if len(masks) > 1:
masks = numpy.logical_and(*masks)
else:
masks = masks[0]
retu... | A :class:`~Fred2.Core.Result.TAPPredictionResult` object is a :class:`pandas.DataFrame` with single-indexing, where column Ids are the ` prediction names of the different prediction methods, and row ID the :class:`~Fred2.Core.Peptide.Peptide` object TAPPredictionResult: +--------------+-------------+ | Peptide Obj | Me... | TAPPredictionResult | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TAPPredictionResult:
"""A :class:`~Fred2.Core.Result.TAPPredictionResult` object is a :class:`pandas.DataFrame` with single-indexing, where column Ids are the ` prediction names of the different prediction methods, and row ID the :class:`~Fred2.Core.Peptide.Peptide` object TAPPredictionResult: +-... | stack_v2_sparse_classes_10k_train_005204 | 14,645 | permissive | [
{
"docstring": "Filters a result data frame based on a specified expression consisting of a list of triple with (method_name, comparator, threshold). The expression is applied to each row. If any of the columns fulfill the criteria the row remains. :param list((str,comparator,float)) expressions: A list of trip... | 2 | null | Implement the Python class `TAPPredictionResult` described below.
Class description:
A :class:`~Fred2.Core.Result.TAPPredictionResult` object is a :class:`pandas.DataFrame` with single-indexing, where column Ids are the ` prediction names of the different prediction methods, and row ID the :class:`~Fred2.Core.Peptide.... | Implement the Python class `TAPPredictionResult` described below.
Class description:
A :class:`~Fred2.Core.Result.TAPPredictionResult` object is a :class:`pandas.DataFrame` with single-indexing, where column Ids are the ` prediction names of the different prediction methods, and row ID the :class:`~Fred2.Core.Peptide.... | b3e54c8c4ed12b780b61f74672e9667245a7bb78 | <|skeleton|>
class TAPPredictionResult:
"""A :class:`~Fred2.Core.Result.TAPPredictionResult` object is a :class:`pandas.DataFrame` with single-indexing, where column Ids are the ` prediction names of the different prediction methods, and row ID the :class:`~Fred2.Core.Peptide.Peptide` object TAPPredictionResult: +-... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TAPPredictionResult:
"""A :class:`~Fred2.Core.Result.TAPPredictionResult` object is a :class:`pandas.DataFrame` with single-indexing, where column Ids are the ` prediction names of the different prediction methods, and row ID the :class:`~Fred2.Core.Peptide.Peptide` object TAPPredictionResult: +--------------... | the_stack_v2_python_sparse | Fred2/Core/Result.py | FRED-2/Fred2 | train | 42 |
a53b851c71f2701570b04278c4125d898602d0d6 | [
"kwargs['nargs'] = -1\ndefault = kwargs.pop('default', tuple())\nsuper().__init__(*args, **kwargs)\nself.default = default",
"if not value:\n value = self.default\nelse:\n value = [self._parse_arg_str(i) for i in value]\nreturn super().process_value(ctx, value)",
"parsed = ast.literal_eval(args)\nif not i... | <|body_start_0|>
kwargs['nargs'] = -1
default = kwargs.pop('default', tuple())
super().__init__(*args, **kwargs)
self.default = default
<|end_body_0|>
<|body_start_1|>
if not value:
value = self.default
else:
value = [self._parse_arg_str(i) for i ... | Multiple arguments with default value. | DefaultArgumentsMultiple | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DefaultArgumentsMultiple:
"""Multiple arguments with default value."""
def __init__(self, *args: Any, **kwargs: Any) -> None:
"""Create MultipleArguments instance."""
<|body_0|>
def full_process_value(self, ctx: Context, value: Any) -> Any:
"""Given a value and c... | stack_v2_sparse_classes_10k_train_005205 | 10,471 | permissive | [
{
"docstring": "Create MultipleArguments instance.",
"name": "__init__",
"signature": "def __init__(self, *args: Any, **kwargs: Any) -> None"
},
{
"docstring": "Given a value and context this runs the logic to convert the value as necessary. :param ctx: command context :param value: value for op... | 3 | null | Implement the Python class `DefaultArgumentsMultiple` described below.
Class description:
Multiple arguments with default value.
Method signatures and docstrings:
- def __init__(self, *args: Any, **kwargs: Any) -> None: Create MultipleArguments instance.
- def full_process_value(self, ctx: Context, value: Any) -> Any... | Implement the Python class `DefaultArgumentsMultiple` described below.
Class description:
Multiple arguments with default value.
Method signatures and docstrings:
- def __init__(self, *args: Any, **kwargs: Any) -> None: Create MultipleArguments instance.
- def full_process_value(self, ctx: Context, value: Any) -> Any... | bec49adaeba661d8d0f03ac9935dc89f39d95a0d | <|skeleton|>
class DefaultArgumentsMultiple:
"""Multiple arguments with default value."""
def __init__(self, *args: Any, **kwargs: Any) -> None:
"""Create MultipleArguments instance."""
<|body_0|>
def full_process_value(self, ctx: Context, value: Any) -> Any:
"""Given a value and c... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class DefaultArgumentsMultiple:
"""Multiple arguments with default value."""
def __init__(self, *args: Any, **kwargs: Any) -> None:
"""Create MultipleArguments instance."""
kwargs['nargs'] = -1
default = kwargs.pop('default', tuple())
super().__init__(*args, **kwargs)
se... | the_stack_v2_python_sparse | benchmark/framework/cli.py | fetchai/agents-aea | train | 192 |
dafeb5ffc6685f724a581d5e9d23054b7d161d71 | [
"pos_bboxes_list = [res.pos_bboxes for res in sampling_results]\nneg_bboxes_list = [res.neg_bboxes for res in sampling_results]\npos_gt_bboxes_list = [res.pos_gt_bboxes for res in sampling_results]\npos_gt_labels_list = [res.pos_gt_labels for res in sampling_results]\nlabels, label_weights, bbox_targets, bbox_weigh... | <|body_start_0|>
pos_bboxes_list = [res.pos_bboxes for res in sampling_results]
neg_bboxes_list = [res.neg_bboxes for res in sampling_results]
pos_gt_bboxes_list = [res.pos_gt_bboxes for res in sampling_results]
pos_gt_labels_list = [res.pos_gt_labels for res in sampling_results]
... | CustomConvFCBBoxHead class for OTX. | CustomConvFCBBoxHead | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CustomConvFCBBoxHead:
"""CustomConvFCBBoxHead class for OTX."""
def get_targets(self, sampling_results, gt_bboxes, gt_labels, img_metas, rcnn_train_cfg, concat=True):
"""Calculate the ground truth for all samples in a batch according to the sampling_results. Almost the same as the im... | stack_v2_sparse_classes_10k_train_005206 | 8,559 | permissive | [
{
"docstring": "Calculate the ground truth for all samples in a batch according to the sampling_results. Almost the same as the implementation in bbox_head, we passed additional parameters pos_inds_list and neg_inds_list to `_get_target_single` function. Args: sampling_results (List[obj:SamplingResults]): Assig... | 2 | null | Implement the Python class `CustomConvFCBBoxHead` described below.
Class description:
CustomConvFCBBoxHead class for OTX.
Method signatures and docstrings:
- def get_targets(self, sampling_results, gt_bboxes, gt_labels, img_metas, rcnn_train_cfg, concat=True): Calculate the ground truth for all samples in a batch acc... | Implement the Python class `CustomConvFCBBoxHead` described below.
Class description:
CustomConvFCBBoxHead class for OTX.
Method signatures and docstrings:
- def get_targets(self, sampling_results, gt_bboxes, gt_labels, img_metas, rcnn_train_cfg, concat=True): Calculate the ground truth for all samples in a batch acc... | 80454808b38727e358e8b880043eeac0f18152fb | <|skeleton|>
class CustomConvFCBBoxHead:
"""CustomConvFCBBoxHead class for OTX."""
def get_targets(self, sampling_results, gt_bboxes, gt_labels, img_metas, rcnn_train_cfg, concat=True):
"""Calculate the ground truth for all samples in a batch according to the sampling_results. Almost the same as the im... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class CustomConvFCBBoxHead:
"""CustomConvFCBBoxHead class for OTX."""
def get_targets(self, sampling_results, gt_bboxes, gt_labels, img_metas, rcnn_train_cfg, concat=True):
"""Calculate the ground truth for all samples in a batch according to the sampling_results. Almost the same as the implementation ... | the_stack_v2_python_sparse | src/otx/algorithms/detection/adapters/mmdet/models/heads/custom_roi_head.py | openvinotoolkit/training_extensions | train | 397 |
c65202139a2349f4634690195a93f3f433673a1d | [
"import_info.pop(CONF_MONITORED_CONDITIONS, None)\nimport_info.pop(CONF_NICS, None)\nimport_info.pop(CONF_DRIVES, None)\nimport_info.pop(CONF_VOLUMES, None)\nreturn await self.async_step_user(import_info)",
"errors = {}\nif user_input is not None:\n host = user_input[CONF_HOST]\n protocol = 'https' if user_... | <|body_start_0|>
import_info.pop(CONF_MONITORED_CONDITIONS, None)
import_info.pop(CONF_NICS, None)
import_info.pop(CONF_DRIVES, None)
import_info.pop(CONF_VOLUMES, None)
return await self.async_step_user(import_info)
<|end_body_0|>
<|body_start_1|>
errors = {}
if... | Qnap configuration flow. | QnapConfigFlow | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class QnapConfigFlow:
"""Qnap configuration flow."""
async def async_step_import(self, import_info: dict[str, Any]) -> FlowResult:
"""Set the config entry up from yaml."""
<|body_0|>
async def async_step_user(self, user_input: dict[str, Any] | None=None) -> FlowResult:
... | stack_v2_sparse_classes_10k_train_005207 | 3,220 | permissive | [
{
"docstring": "Set the config entry up from yaml.",
"name": "async_step_import",
"signature": "async def async_step_import(self, import_info: dict[str, Any]) -> FlowResult"
},
{
"docstring": "Handle a flow initialized by the user.",
"name": "async_step_user",
"signature": "async def asy... | 2 | stack_v2_sparse_classes_30k_train_006153 | Implement the Python class `QnapConfigFlow` described below.
Class description:
Qnap configuration flow.
Method signatures and docstrings:
- async def async_step_import(self, import_info: dict[str, Any]) -> FlowResult: Set the config entry up from yaml.
- async def async_step_user(self, user_input: dict[str, Any] | N... | Implement the Python class `QnapConfigFlow` described below.
Class description:
Qnap configuration flow.
Method signatures and docstrings:
- async def async_step_import(self, import_info: dict[str, Any]) -> FlowResult: Set the config entry up from yaml.
- async def async_step_user(self, user_input: dict[str, Any] | N... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class QnapConfigFlow:
"""Qnap configuration flow."""
async def async_step_import(self, import_info: dict[str, Any]) -> FlowResult:
"""Set the config entry up from yaml."""
<|body_0|>
async def async_step_user(self, user_input: dict[str, Any] | None=None) -> FlowResult:
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class QnapConfigFlow:
"""Qnap configuration flow."""
async def async_step_import(self, import_info: dict[str, Any]) -> FlowResult:
"""Set the config entry up from yaml."""
import_info.pop(CONF_MONITORED_CONDITIONS, None)
import_info.pop(CONF_NICS, None)
import_info.pop(CONF_DRIV... | the_stack_v2_python_sparse | homeassistant/components/qnap/config_flow.py | home-assistant/core | train | 35,501 |
0eb09798c0258e0ed3a5b4bbaaa575446bdc483d | [
"self.frame_type_link = frame_type_link\nself.a_frame_inst = a_frame_inst\nself.b_frame_inst = b_frame_inst\nself.frame_inst_arg_links = []\na_frame_inst.link = self\nb_frame_inst.link = self\nself._link_args()",
"a_frame_inst_args = self.a_frame_inst.args\nb_frame_inst_args = self.b_frame_inst.args\nfor a_frame_... | <|body_start_0|>
self.frame_type_link = frame_type_link
self.a_frame_inst = a_frame_inst
self.b_frame_inst = b_frame_inst
self.frame_inst_arg_links = []
a_frame_inst.link = self
b_frame_inst.link = self
self._link_args()
<|end_body_0|>
<|body_start_1|>
a_... | Frame_inst_link | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Frame_inst_link:
def __init__(self, frame_type_link, a_frame_inst, b_frame_inst):
"""called from Frame_type_link.link_frame_insts"""
<|body_0|>
def _link_args(self):
"""called from __init__"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.frame_... | stack_v2_sparse_classes_10k_train_005208 | 6,044 | no_license | [
{
"docstring": "called from Frame_type_link.link_frame_insts",
"name": "__init__",
"signature": "def __init__(self, frame_type_link, a_frame_inst, b_frame_inst)"
},
{
"docstring": "called from __init__",
"name": "_link_args",
"signature": "def _link_args(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_000676 | Implement the Python class `Frame_inst_link` described below.
Class description:
Implement the Frame_inst_link class.
Method signatures and docstrings:
- def __init__(self, frame_type_link, a_frame_inst, b_frame_inst): called from Frame_type_link.link_frame_insts
- def _link_args(self): called from __init__ | Implement the Python class `Frame_inst_link` described below.
Class description:
Implement the Frame_inst_link class.
Method signatures and docstrings:
- def __init__(self, frame_type_link, a_frame_inst, b_frame_inst): called from Frame_type_link.link_frame_insts
- def _link_args(self): called from __init__
<|skelet... | 194446ec1adeec5ef85db3f96b6d8d2876cc8811 | <|skeleton|>
class Frame_inst_link:
def __init__(self, frame_type_link, a_frame_inst, b_frame_inst):
"""called from Frame_type_link.link_frame_insts"""
<|body_0|>
def _link_args(self):
"""called from __init__"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Frame_inst_link:
def __init__(self, frame_type_link, a_frame_inst, b_frame_inst):
"""called from Frame_type_link.link_frame_insts"""
self.frame_type_link = frame_type_link
self.a_frame_inst = a_frame_inst
self.b_frame_inst = b_frame_inst
self.frame_inst_arg_links = []
... | the_stack_v2_python_sparse | udapi-python/udapi/block/valency/link_structures.py | Jankus1994/ud-valency | train | 0 | |
47a8a465a6a83d067d67917f1fdbb99fe4afc63c | [
"if len(li) <= 0:\n return False\nif len(li) == 1:\n return ListNode(li[0])\nelse:\n root = ListNode(li[0])\n tmp = root\n for i in range(1, len(li)):\n tmp.next = ListNode(li[i])\n tmp = tmp.next\n return root",
"value = []\ntmp = root\nwhile tmp.next != None:\n value.append(st... | <|body_start_0|>
if len(li) <= 0:
return False
if len(li) == 1:
return ListNode(li[0])
else:
root = ListNode(li[0])
tmp = root
for i in range(1, len(li)):
tmp.next = ListNode(li[i])
tmp = tmp.next
... | ListNode_handle | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ListNode_handle:
def Creatlist(self, li):
"""从列表创建一个链表 :param li: 列表 :return: 头结点"""
<|body_0|>
def print_linked(self, root: ListNode):
"""打印链表 :param root: 头结点 :return: 打印链表"""
<|body_1|>
def length(self, root):
"""计算链表的长度 :param root: :return:"... | stack_v2_sparse_classes_10k_train_005209 | 3,869 | no_license | [
{
"docstring": "从列表创建一个链表 :param li: 列表 :return: 头结点",
"name": "Creatlist",
"signature": "def Creatlist(self, li)"
},
{
"docstring": "打印链表 :param root: 头结点 :return: 打印链表",
"name": "print_linked",
"signature": "def print_linked(self, root: ListNode)"
},
{
"docstring": "计算链表的长度 :pa... | 5 | stack_v2_sparse_classes_30k_train_002753 | Implement the Python class `ListNode_handle` described below.
Class description:
Implement the ListNode_handle class.
Method signatures and docstrings:
- def Creatlist(self, li): 从列表创建一个链表 :param li: 列表 :return: 头结点
- def print_linked(self, root: ListNode): 打印链表 :param root: 头结点 :return: 打印链表
- def length(self, root)... | Implement the Python class `ListNode_handle` described below.
Class description:
Implement the ListNode_handle class.
Method signatures and docstrings:
- def Creatlist(self, li): 从列表创建一个链表 :param li: 列表 :return: 头结点
- def print_linked(self, root: ListNode): 打印链表 :param root: 头结点 :return: 打印链表
- def length(self, root)... | 2ef266ee3175d08d125151c9983b864e6ed3343b | <|skeleton|>
class ListNode_handle:
def Creatlist(self, li):
"""从列表创建一个链表 :param li: 列表 :return: 头结点"""
<|body_0|>
def print_linked(self, root: ListNode):
"""打印链表 :param root: 头结点 :return: 打印链表"""
<|body_1|>
def length(self, root):
"""计算链表的长度 :param root: :return:"... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ListNode_handle:
def Creatlist(self, li):
"""从列表创建一个链表 :param li: 列表 :return: 头结点"""
if len(li) <= 0:
return False
if len(li) == 1:
return ListNode(li[0])
else:
root = ListNode(li[0])
tmp = root
for i in range(1, len(l... | the_stack_v2_python_sparse | leetcode/0001-0100/0019.删除链表的倒数第n个结点.py | alpharol/algorithm_python3 | train | 1 | |
87cc971945e216c065217371ffee3ebaca09f943 | [
"entry_frm = form.FormNode(self.name)\nentry_frm(value=getattr(storable, self.get_column_name(), self.get('default_value', '')))\nif style == 'listing' or self.get('read_only', False):\n entry_frm(type='label')\n if self.get('obfuscate', True):\n entry_frm(value='********')\n return entry_frm\nelif ... | <|body_start_0|>
entry_frm = form.FormNode(self.name)
entry_frm(value=getattr(storable, self.get_column_name(), self.get('default_value', '')))
if style == 'listing' or self.get('read_only', False):
entry_frm(type='label')
if self.get('obfuscate', True):
e... | Allow editing of an optionally encrypted password field. | PasswordField | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PasswordField:
"""Allow editing of an optionally encrypted password field."""
def get_element(self, req, style, storable):
"""@see: L{modu.editable.define.definition.get_element()}"""
<|body_0|>
def update_storable(self, req, form, storable):
"""@see: L{modu.edit... | stack_v2_sparse_classes_10k_train_005210 | 6,918 | permissive | [
{
"docstring": "@see: L{modu.editable.define.definition.get_element()}",
"name": "get_element",
"signature": "def get_element(self, req, style, storable)"
},
{
"docstring": "@see: L{modu.editable.define.definition.update_storable()}",
"name": "update_storable",
"signature": "def update_s... | 2 | stack_v2_sparse_classes_30k_train_001082 | Implement the Python class `PasswordField` described below.
Class description:
Allow editing of an optionally encrypted password field.
Method signatures and docstrings:
- def get_element(self, req, style, storable): @see: L{modu.editable.define.definition.get_element()}
- def update_storable(self, req, form, storabl... | Implement the Python class `PasswordField` described below.
Class description:
Allow editing of an optionally encrypted password field.
Method signatures and docstrings:
- def get_element(self, req, style, storable): @see: L{modu.editable.define.definition.get_element()}
- def update_storable(self, req, form, storabl... | 795f3bc413956b98522ac514dafe35cbab0d57a3 | <|skeleton|>
class PasswordField:
"""Allow editing of an optionally encrypted password field."""
def get_element(self, req, style, storable):
"""@see: L{modu.editable.define.definition.get_element()}"""
<|body_0|>
def update_storable(self, req, form, storable):
"""@see: L{modu.edit... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PasswordField:
"""Allow editing of an optionally encrypted password field."""
def get_element(self, req, style, storable):
"""@see: L{modu.editable.define.definition.get_element()}"""
entry_frm = form.FormNode(self.name)
entry_frm(value=getattr(storable, self.get_column_name(), se... | the_stack_v2_python_sparse | src/modu/editable/datatypes/string.py | philchristensen/modu | train | 0 |
d8a171b8c2a82b1ea59cb6027c59d1c995dd657b | [
"def helper(p1, p2):\n if p1 and p2:\n return p1.val == p2.val and helper(p1.left, p2.right) and helper(p1.right, p2.left)\n else:\n return p1 is p2\nif root is None:\n return True\nelse:\n return helper(root.left, root.right)",
"if root is None:\n return True\np1 = root.left\np2 = ro... | <|body_start_0|>
def helper(p1, p2):
if p1 and p2:
return p1.val == p2.val and helper(p1.left, p2.right) and helper(p1.right, p2.left)
else:
return p1 is p2
if root is None:
return True
else:
return helper(root.left,... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isSymmetric(self, root):
"""When need to compare left and right part at same level together, than separate original tree to several subTrees is a solution. :type root: TreeNode :rtype: bool"""
<|body_0|>
def isSymmetricIterative(self, root):
"""Using it... | stack_v2_sparse_classes_10k_train_005211 | 1,950 | no_license | [
{
"docstring": "When need to compare left and right part at same level together, than separate original tree to several subTrees is a solution. :type root: TreeNode :rtype: bool",
"name": "isSymmetric",
"signature": "def isSymmetric(self, root)"
},
{
"docstring": "Using iterative :param root: :r... | 2 | stack_v2_sparse_classes_30k_train_001212 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isSymmetric(self, root): When need to compare left and right part at same level together, than separate original tree to several subTrees is a solution. :type root: TreeNode ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isSymmetric(self, root): When need to compare left and right part at same level together, than separate original tree to several subTrees is a solution. :type root: TreeNode ... | 11d6bf2ba7b50c07e048df37c4e05c8f46b92241 | <|skeleton|>
class Solution:
def isSymmetric(self, root):
"""When need to compare left and right part at same level together, than separate original tree to several subTrees is a solution. :type root: TreeNode :rtype: bool"""
<|body_0|>
def isSymmetricIterative(self, root):
"""Using it... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def isSymmetric(self, root):
"""When need to compare left and right part at same level together, than separate original tree to several subTrees is a solution. :type root: TreeNode :rtype: bool"""
def helper(p1, p2):
if p1 and p2:
return p1.val == p2.val a... | the_stack_v2_python_sparse | LeetCodes/DFS/SymmetricTree.py | chutianwen/LeetCodes | train | 0 | |
0d4f7d61f4a35c62f973ef175267e9b3999931d0 | [
"self.caffe = Caffe.objects.create(name='kafo', city='Gliwice', street='Wieczorka', house_number='14', postal_code='44-100')\nself.filtry = Caffe.objects.create(name='filtry', city='Warszawa', street='Filry', house_number='14', postal_code='44-100')\nself.bakery = Company.objects.create(name='bakery', caffe=self.ca... | <|body_start_0|>
self.caffe = Caffe.objects.create(name='kafo', city='Gliwice', street='Wieczorka', house_number='14', postal_code='44-100')
self.filtry = Caffe.objects.create(name='filtry', city='Warszawa', street='Filry', house_number='14', postal_code='44-100')
self.bakery = Company.objects.c... | Company model tests. | CompanyModelTest | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CompanyModelTest:
"""Company model tests."""
def setUp(self):
"""Test data setup."""
<|body_0|>
def test_name(self):
"""Check if name is unique across one caffe."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.caffe = Caffe.objects.create(n... | stack_v2_sparse_classes_10k_train_005212 | 8,665 | permissive | [
{
"docstring": "Test data setup.",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Check if name is unique across one caffe.",
"name": "test_name",
"signature": "def test_name(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003230 | Implement the Python class `CompanyModelTest` described below.
Class description:
Company model tests.
Method signatures and docstrings:
- def setUp(self): Test data setup.
- def test_name(self): Check if name is unique across one caffe. | Implement the Python class `CompanyModelTest` described below.
Class description:
Company model tests.
Method signatures and docstrings:
- def setUp(self): Test data setup.
- def test_name(self): Check if name is unique across one caffe.
<|skeleton|>
class CompanyModelTest:
"""Company model tests."""
def se... | cdb7f5edb29255c7e874eaa6231621063210a8b0 | <|skeleton|>
class CompanyModelTest:
"""Company model tests."""
def setUp(self):
"""Test data setup."""
<|body_0|>
def test_name(self):
"""Check if name is unique across one caffe."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class CompanyModelTest:
"""Company model tests."""
def setUp(self):
"""Test data setup."""
self.caffe = Caffe.objects.create(name='kafo', city='Gliwice', street='Wieczorka', house_number='14', postal_code='44-100')
self.filtry = Caffe.objects.create(name='filtry', city='Warszawa', stree... | the_stack_v2_python_sparse | caffe/cash/test_models.py | VirrageS/io-kawiarnie | train | 3 |
619f2cff6e97da7a8707040c73527b2da13c2190 | [
"if len(xcols) == 0 or xcols is None:\n return 0.0\nsample_size = len(df.index)\ndf1 = df.copy()\ndf1 = df1.groupby(xcols)[xcols].size()\ndf1 = df1.apply(lambda x: x / sample_size)\nlocal_ent = -df1 * np.log(df1 + 1e-07)\nall_ent = local_ent.sum()\nif verbose:\n print('\\nprobs for ', xcols)\n print(df1)\n... | <|body_start_0|>
if len(xcols) == 0 or xcols is None:
return 0.0
sample_size = len(df.index)
df1 = df.copy()
df1 = df1.groupby(xcols)[xcols].size()
df1 = df1.apply(lambda x: x / sample_size)
local_ent = -df1 * np.log(df1 + 1e-07)
all_ent = local_ent.su... | This class calculates classical (not quantum yet) entropy, conditional information (CI), mutual information (MI) and conditional mutual info ( CMI) from a dataframe (hence, from empirical data, not from the true distributions of a bnet). logs's in entropies are base e, natural logs Error in entropy is ln(n+1) - ln(n) ... | DataEntropy | [
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DataEntropy:
"""This class calculates classical (not quantum yet) entropy, conditional information (CI), mutual information (MI) and conditional mutual info ( CMI) from a dataframe (hence, from empirical data, not from the true distributions of a bnet). logs's in entropies are base e, natural log... | stack_v2_sparse_classes_10k_train_005213 | 5,703 | permissive | [
{
"docstring": "Returns the entropy H(x) where x is given by the list of columns xcols in the dataframe df. Parameters ---------- df : pandas.DataFrame dataframe for which entropy is calculated xcols : list[str] list of column names in df. The x in H(x) verbose : bool If True, print extra info in console. Retur... | 4 | stack_v2_sparse_classes_30k_train_002373 | Implement the Python class `DataEntropy` described below.
Class description:
This class calculates classical (not quantum yet) entropy, conditional information (CI), mutual information (MI) and conditional mutual info ( CMI) from a dataframe (hence, from empirical data, not from the true distributions of a bnet). logs... | Implement the Python class `DataEntropy` described below.
Class description:
This class calculates classical (not quantum yet) entropy, conditional information (CI), mutual information (MI) and conditional mutual info ( CMI) from a dataframe (hence, from empirical data, not from the true distributions of a bnet). logs... | 5b4a3055ea14c2ee9c80c339f759fe2b9c8c51e2 | <|skeleton|>
class DataEntropy:
"""This class calculates classical (not quantum yet) entropy, conditional information (CI), mutual information (MI) and conditional mutual info ( CMI) from a dataframe (hence, from empirical data, not from the true distributions of a bnet). logs's in entropies are base e, natural log... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class DataEntropy:
"""This class calculates classical (not quantum yet) entropy, conditional information (CI), mutual information (MI) and conditional mutual info ( CMI) from a dataframe (hence, from empirical data, not from the true distributions of a bnet). logs's in entropies are base e, natural logs Error in en... | the_stack_v2_python_sparse | shannon_info_theory/DataEntropy.py | artiste-qb-net/quantum-fog | train | 95 |
5438d06bdd1830e1613cd34df1fb13235798e29b | [
"for i in range(len(s)):\n t = s[:i] + s[i + 1:]\n if t == t[::-1]:\n return True\nreturn s == s[::-1]",
"def is_pali_range(i, j):\n return all((s[k] == s[j - k + i] for k in range(i, j)))\nfor i in range(len(s) / 2):\n if s[i] != s[~i]:\n j = len(s) - 1 - i\n return is_pali_range... | <|body_start_0|>
for i in range(len(s)):
t = s[:i] + s[i + 1:]
if t == t[::-1]:
return True
return s == s[::-1]
<|end_body_0|>
<|body_start_1|>
def is_pali_range(i, j):
return all((s[k] == s[j - k + i] for k in range(i, j)))
for i in r... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def validPalindrome(self, s: str) -> bool:
"""For each index i in the given string, let's remove that character, then check if the resulting string is a palindrome. If it is, (or if the original string was a palindrome), then we'll return true"""
<|body_0|>
def val... | stack_v2_sparse_classes_10k_train_005214 | 1,977 | no_license | [
{
"docstring": "For each index i in the given string, let's remove that character, then check if the resulting string is a palindrome. If it is, (or if the original string was a palindrome), then we'll return true",
"name": "validPalindrome",
"signature": "def validPalindrome(self, s: str) -> bool"
},... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def validPalindrome(self, s: str) -> bool: For each index i in the given string, let's remove that character, then check if the resulting string is a palindrome. If it is, (or if... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def validPalindrome(self, s: str) -> bool: For each index i in the given string, let's remove that character, then check if the resulting string is a palindrome. If it is, (or if... | 727dec2e23e765925a5e7e003fc99aeaf25111e9 | <|skeleton|>
class Solution:
def validPalindrome(self, s: str) -> bool:
"""For each index i in the given string, let's remove that character, then check if the resulting string is a palindrome. If it is, (or if the original string was a palindrome), then we'll return true"""
<|body_0|>
def val... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def validPalindrome(self, s: str) -> bool:
"""For each index i in the given string, let's remove that character, then check if the resulting string is a palindrome. If it is, (or if the original string was a palindrome), then we'll return true"""
for i in range(len(s)):
t... | the_stack_v2_python_sparse | funNLearn/src/main/java/dsAlgo/leetcode/P6xx/P680_ValidPalindromeII.py | vishalpmittal/practice-fun | train | 0 | |
25385a28f0f74829106862d6042db499eda90493 | [
"self._has_been_validated: bool = False\nself._source_names: Set[SourceName] = {GITHUB, GITLAB, BITBUCKET}\nself.protocol_override: Optional[Protocol] = None\nself._sources: Dict[SourceName, Source] = {GITHUB: Source(GITHUB, GITHUB_YAML), GITLAB: Source(GITLAB, GITLAB_YAML), BITBUCKET: Source(BITBUCKET, BITBUCKET_Y... | <|body_start_0|>
self._has_been_validated: bool = False
self._source_names: Set[SourceName] = {GITHUB, GITLAB, BITBUCKET}
self.protocol_override: Optional[Protocol] = None
self._sources: Dict[SourceName, Source] = {GITHUB: Source(GITHUB, GITHUB_YAML), GITLAB: Source(GITLAB, GITLAB_YAML),... | Class encapsulating project information from clowder yaml for controlling clowder :ivar Optional[str] protocol_override: The protocol to override sources without an explicitly specified protcol | SourceController | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SourceController:
"""Class encapsulating project information from clowder yaml for controlling clowder :ivar Optional[str] protocol_override: The protocol to override sources without an explicitly specified protcol"""
def __init__(self):
"""SourceController __init__"""
<|body... | stack_v2_sparse_classes_10k_train_005215 | 3,873 | permissive | [
{
"docstring": "SourceController __init__",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Register source with controller :param Optional[Union[Source, SourceName]] source: Source to add :raise SourcesValidatedError: :raise UnknownTypeError:",
"name": "add_source",... | 5 | stack_v2_sparse_classes_30k_train_005863 | Implement the Python class `SourceController` described below.
Class description:
Class encapsulating project information from clowder yaml for controlling clowder :ivar Optional[str] protocol_override: The protocol to override sources without an explicitly specified protcol
Method signatures and docstrings:
- def __... | Implement the Python class `SourceController` described below.
Class description:
Class encapsulating project information from clowder yaml for controlling clowder :ivar Optional[str] protocol_override: The protocol to override sources without an explicitly specified protcol
Method signatures and docstrings:
- def __... | 1438fc8b1bb7379de66142ffcb0e20b459b59159 | <|skeleton|>
class SourceController:
"""Class encapsulating project information from clowder yaml for controlling clowder :ivar Optional[str] protocol_override: The protocol to override sources without an explicitly specified protcol"""
def __init__(self):
"""SourceController __init__"""
<|body... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SourceController:
"""Class encapsulating project information from clowder yaml for controlling clowder :ivar Optional[str] protocol_override: The protocol to override sources without an explicitly specified protcol"""
def __init__(self):
"""SourceController __init__"""
self._has_been_vali... | the_stack_v2_python_sparse | clowder/controller/source_controller.py | JrGoodle/clowder | train | 17 |
2c211f1fb3b4ccf847ff2a4dbbe6683c999d96f0 | [
"revoked_token = self._client.revoke_token(token=token)\nif check:\n self.check_token_is_revoked(revoked_token, must_revoked=False)\nreturn revoked_token",
"def predicate():\n try:\n self.get_token_validate(token)\n is_revoked = True\n except exceptions.NotFound:\n is_revoked = False... | <|body_start_0|>
revoked_token = self._client.revoke_token(token=token)
if check:
self.check_token_is_revoked(revoked_token, must_revoked=False)
return revoked_token
<|end_body_0|>
<|body_start_1|>
def predicate():
try:
self.get_token_validate(tok... | Token steps. | TokenSteps | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TokenSteps:
"""Token steps."""
def revoke_token(self, token, check=True):
"""Step to revoke a token. Args: token (str): The token to be revoked. check (bool): flag whether to check step or not Returns: keystoneclient.access.AccessInfo: token"""
<|body_0|>
def check_token... | stack_v2_sparse_classes_10k_train_005216 | 4,425 | no_license | [
{
"docstring": "Step to revoke a token. Args: token (str): The token to be revoked. check (bool): flag whether to check step or not Returns: keystoneclient.access.AccessInfo: token",
"name": "revoke_token",
"signature": "def revoke_token(self, token, check=True)"
},
{
"docstring": "Step to check... | 4 | null | Implement the Python class `TokenSteps` described below.
Class description:
Token steps.
Method signatures and docstrings:
- def revoke_token(self, token, check=True): Step to revoke a token. Args: token (str): The token to be revoked. check (bool): flag whether to check step or not Returns: keystoneclient.access.Acc... | Implement the Python class `TokenSteps` described below.
Class description:
Token steps.
Method signatures and docstrings:
- def revoke_token(self, token, check=True): Step to revoke a token. Args: token (str): The token to be revoked. check (bool): flag whether to check step or not Returns: keystoneclient.access.Acc... | e7583444cd24893ec6ae237b47db7c605b99b0c5 | <|skeleton|>
class TokenSteps:
"""Token steps."""
def revoke_token(self, token, check=True):
"""Step to revoke a token. Args: token (str): The token to be revoked. check (bool): flag whether to check step or not Returns: keystoneclient.access.AccessInfo: token"""
<|body_0|>
def check_token... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TokenSteps:
"""Token steps."""
def revoke_token(self, token, check=True):
"""Step to revoke a token. Args: token (str): The token to be revoked. check (bool): flag whether to check step or not Returns: keystoneclient.access.AccessInfo: token"""
revoked_token = self._client.revoke_token(to... | the_stack_v2_python_sparse | stepler/keystone/steps/tokens.py | Mirantis/stepler | train | 16 |
0c3bce9b67a34ced9fc31d42e5e753770d9e0ebe | [
"if name is not None:\n pulumi.set(__self__, 'name', name)\nif value is not None:\n pulumi.set(__self__, 'value', value)",
"warnings.warn(\"Field 'parameters' has been deprecated from version 1.101.0. Use 'config' instead.\", DeprecationWarning)\npulumi.log.warn(\"name is deprecated: Field 'parameters' has ... | <|body_start_0|>
if name is not None:
pulumi.set(__self__, 'name', name)
if value is not None:
pulumi.set(__self__, 'value', value)
<|end_body_0|>
<|body_start_1|>
warnings.warn("Field 'parameters' has been deprecated from version 1.101.0. Use 'config' instead.", Depreca... | InstanceParameter | [
"Apache-2.0",
"MPL-2.0",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InstanceParameter:
def __init__(__self__, *, name: Optional[str]=None, value: Optional[str]=None):
""":param str name: Field `parameters` has been deprecated from provider version 1.101.0 and `config` instead. :param str value: Field `parameters` has been deprecated from provider version... | stack_v2_sparse_classes_10k_train_005217 | 32,429 | permissive | [
{
"docstring": ":param str name: Field `parameters` has been deprecated from provider version 1.101.0 and `config` instead. :param str value: Field `parameters` has been deprecated from provider version 1.101.0 and `config` instead.",
"name": "__init__",
"signature": "def __init__(__self__, *, name: Opt... | 3 | null | Implement the Python class `InstanceParameter` described below.
Class description:
Implement the InstanceParameter class.
Method signatures and docstrings:
- def __init__(__self__, *, name: Optional[str]=None, value: Optional[str]=None): :param str name: Field `parameters` has been deprecated from provider version 1.... | Implement the Python class `InstanceParameter` described below.
Class description:
Implement the InstanceParameter class.
Method signatures and docstrings:
- def __init__(__self__, *, name: Optional[str]=None, value: Optional[str]=None): :param str name: Field `parameters` has been deprecated from provider version 1.... | ffddb9036f7893fbd58863d8364a4977eb1bee17 | <|skeleton|>
class InstanceParameter:
def __init__(__self__, *, name: Optional[str]=None, value: Optional[str]=None):
""":param str name: Field `parameters` has been deprecated from provider version 1.101.0 and `config` instead. :param str value: Field `parameters` has been deprecated from provider version... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class InstanceParameter:
def __init__(__self__, *, name: Optional[str]=None, value: Optional[str]=None):
""":param str name: Field `parameters` has been deprecated from provider version 1.101.0 and `config` instead. :param str value: Field `parameters` has been deprecated from provider version 1.101.0 and `... | the_stack_v2_python_sparse | sdk/python/pulumi_alicloud/kvstore/outputs.py | pulumi/pulumi-alicloud | train | 56 | |
4ba3a0702ff1443ae24fcee69062d3a38329a628 | [
"if not head or not head.next:\n return head\ndummy = ListNode(0)\ndummy.next = head\nsize = 0\nwhile head:\n head = head.next\n size += 1\nstep = 1\nwhile step < size:\n curr, tail = (dummy.next, dummy)\n while curr:\n left = curr\n right = self.split(left, step)\n curr = self.s... | <|body_start_0|>
if not head or not head.next:
return head
dummy = ListNode(0)
dummy.next = head
size = 0
while head:
head = head.next
size += 1
step = 1
while step < size:
curr, tail = (dummy.next, dummy)
... | Node | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Node:
def sorted(self, head: 'ListNode') -> 'ListNode':
"""Sorts the given linked list Time Complexity: O(N log N) Space Complexity: O(1) :param head: :return:"""
<|body_0|>
def merge(self, left: 'ListNode', right: ListNode, head: 'ListNode') -> 'ListNode':
"""Merges... | stack_v2_sparse_classes_10k_train_005218 | 2,250 | no_license | [
{
"docstring": "Sorts the given linked list Time Complexity: O(N log N) Space Complexity: O(1) :param head: :return:",
"name": "sorted",
"signature": "def sorted(self, head: 'ListNode') -> 'ListNode'"
},
{
"docstring": "Merges the left and right after the comparision. :param left: :param right: ... | 3 | null | Implement the Python class `Node` described below.
Class description:
Implement the Node class.
Method signatures and docstrings:
- def sorted(self, head: 'ListNode') -> 'ListNode': Sorts the given linked list Time Complexity: O(N log N) Space Complexity: O(1) :param head: :return:
- def merge(self, left: 'ListNode',... | Implement the Python class `Node` described below.
Class description:
Implement the Node class.
Method signatures and docstrings:
- def sorted(self, head: 'ListNode') -> 'ListNode': Sorts the given linked list Time Complexity: O(N log N) Space Complexity: O(1) :param head: :return:
- def merge(self, left: 'ListNode',... | 65cc78b5afa0db064f9fe8f06597e3e120f7363d | <|skeleton|>
class Node:
def sorted(self, head: 'ListNode') -> 'ListNode':
"""Sorts the given linked list Time Complexity: O(N log N) Space Complexity: O(1) :param head: :return:"""
<|body_0|>
def merge(self, left: 'ListNode', right: ListNode, head: 'ListNode') -> 'ListNode':
"""Merges... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Node:
def sorted(self, head: 'ListNode') -> 'ListNode':
"""Sorts the given linked list Time Complexity: O(N log N) Space Complexity: O(1) :param head: :return:"""
if not head or not head.next:
return head
dummy = ListNode(0)
dummy.next = head
size = 0
... | the_stack_v2_python_sparse | revisited/linked_list/sort_list.py | Shiv2157k/leet_code | train | 1 | |
85f82fd233c92a6959977eda259030bfddc12bc5 | [
"self.out = None\nself.in_shape = None\nself.work_shape = None",
"assert x.ndim > 1, \"prysm's softmax is meant for use with multiple independent variables at once\"\nxx = x.reshape((-1, x.shape[-1]))\nself.in_shape = x.shape\nself.work_shape = xx.shape\nxnorm = xx - xx.max(axis=1)[:, np.newaxis]\ne_x = np.exp(xn... | <|body_start_0|>
self.out = None
self.in_shape = None
self.work_shape = None
<|end_body_0|>
<|body_start_1|>
assert x.ndim > 1, "prysm's softmax is meant for use with multiple independent variables at once"
xx = x.reshape((-1, x.shape[-1]))
self.in_shape = x.shape
... | Softmax activation function. Softmax is a soft, differntiable alternative to argmax. It is used as a component of GumbelSoftmaxEncoder to ecourage / softly force variables to take on one of K discrete states. The arrays passed to forward() and reverse() may take any number of dimensions. The understanding of the inputs... | Softmax | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Softmax:
"""Softmax activation function. Softmax is a soft, differntiable alternative to argmax. It is used as a component of GumbelSoftmaxEncoder to ecourage / softly force variables to take on one of K discrete states. The arrays passed to forward() and reverse() may take any number of dimensio... | stack_v2_sparse_classes_10k_train_005219 | 7,697 | permissive | [
{
"docstring": "Create a new Softmax node.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Perform Softmax activation on logits. Parameters ---------- x : numpy.ndarray, shape (A,B,C, ... K) any number of leading dimensions, required trailing dimension of size K, where... | 3 | stack_v2_sparse_classes_30k_train_004875 | Implement the Python class `Softmax` described below.
Class description:
Softmax activation function. Softmax is a soft, differntiable alternative to argmax. It is used as a component of GumbelSoftmaxEncoder to ecourage / softly force variables to take on one of K discrete states. The arrays passed to forward() and re... | Implement the Python class `Softmax` described below.
Class description:
Softmax activation function. Softmax is a soft, differntiable alternative to argmax. It is used as a component of GumbelSoftmaxEncoder to ecourage / softly force variables to take on one of K discrete states. The arrays passed to forward() and re... | af89c94d500a274eda664188ddb97fcae30c6ac5 | <|skeleton|>
class Softmax:
"""Softmax activation function. Softmax is a soft, differntiable alternative to argmax. It is used as a component of GumbelSoftmaxEncoder to ecourage / softly force variables to take on one of K discrete states. The arrays passed to forward() and reverse() may take any number of dimensio... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Softmax:
"""Softmax activation function. Softmax is a soft, differntiable alternative to argmax. It is used as a component of GumbelSoftmaxEncoder to ecourage / softly force variables to take on one of K discrete states. The arrays passed to forward() and reverse() may take any number of dimensions. The under... | the_stack_v2_python_sparse | prysm/x/optym/activation.py | brandondube/prysm | train | 192 |
06858e122cd77b5879f9ed866db061f9e15a40d8 | [
"if 6 * n < s or n < 1 or s < n:\n return 0\nif n == 1:\n return 1\nreturn self.getNSumCount(n - 1, s - 1) + self.getNSumCount(n - 1, s - 2) + self.getNSumCount(n - 1, s - 3) + self.getNSumCount(n - 1, s - 4) + self.getNSumCount(n - 1, s - 5) + self.getNSumCount(n - 1, s - 6)",
"num = [[0 for j in range(6 *... | <|body_start_0|>
if 6 * n < s or n < 1 or s < n:
return 0
if n == 1:
return 1
return self.getNSumCount(n - 1, s - 1) + self.getNSumCount(n - 1, s - 2) + self.getNSumCount(n - 1, s - 3) + self.getNSumCount(n - 1, s - 4) + self.getNSumCount(n - 1, s - 5) + self.getNSumCount... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def getNSumCount(self, n, s):
"""递归版本 :param n: :param s: :return:"""
<|body_0|>
def getNSumCountNotRecusion(self, n, s):
"""非递归版本 :param n: :param s: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if 6 * n < s or n < 1 or s < n:... | stack_v2_sparse_classes_10k_train_005220 | 1,137 | no_license | [
{
"docstring": "递归版本 :param n: :param s: :return:",
"name": "getNSumCount",
"signature": "def getNSumCount(self, n, s)"
},
{
"docstring": "非递归版本 :param n: :param s: :return:",
"name": "getNSumCountNotRecusion",
"signature": "def getNSumCountNotRecusion(self, n, s)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006577 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def getNSumCount(self, n, s): 递归版本 :param n: :param s: :return:
- def getNSumCountNotRecusion(self, n, s): 非递归版本 :param n: :param s: :return: | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def getNSumCount(self, n, s): 递归版本 :param n: :param s: :return:
- def getNSumCountNotRecusion(self, n, s): 非递归版本 :param n: :param s: :return:
<|skeleton|>
class Solution:
d... | aec68ce90a9fbceaeb855efc2c83c047acbd53b5 | <|skeleton|>
class Solution:
def getNSumCount(self, n, s):
"""递归版本 :param n: :param s: :return:"""
<|body_0|>
def getNSumCountNotRecusion(self, n, s):
"""非递归版本 :param n: :param s: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def getNSumCount(self, n, s):
"""递归版本 :param n: :param s: :return:"""
if 6 * n < s or n < 1 or s < n:
return 0
if n == 1:
return 1
return self.getNSumCount(n - 1, s - 1) + self.getNSumCount(n - 1, s - 2) + self.getNSumCount(n - 1, s - 3) + self... | the_stack_v2_python_sparse | offer/offer 60 n个骰子点数.py | clhchtcjj/Algorithm | train | 5 | |
f97d52871ded62b9f26250f55632c6776ac9fce2 | [
"if paydataobj.GetTrade_type() == 'JSAPI':\n result = WxPayApi.unifiedOrder(paydataobj)\n return result",
"if not (unifiedorderresult.has_key('appid') and unifiedorderresult.has_key('prepay_id') and unifiedorderresult.get('prepay_id')):\n raise WxPayException(u'参数错误')\njsapi = WxPayJsApiPay()\njsapi.SetA... | <|body_start_0|>
if paydataobj.GetTrade_type() == 'JSAPI':
result = WxPayApi.unifiedOrder(paydataobj)
return result
<|end_body_0|>
<|body_start_1|>
if not (unifiedorderresult.has_key('appid') and unifiedorderresult.has_key('prepay_id') and unifiedorderresult.get('prepay_id')):
... | /** * * JSAPI支付实现类 * 该类实现了从微信公众平台获取code、通过code获取openid和access_token、 * 生成jsapi支付js接口所需的参数、生成获取共享收货地址所需的参数 * * 该类是微信支付提供的样例程序，商户可根据自己的需求修改，或者使用lib中的api自行开发 * * @author minkedong * */ | JsApiPay | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class JsApiPay:
"""/** * * JSAPI支付实现类 * 该类实现了从微信公众平台获取code、通过code获取openid和access_token、 * 生成jsapi支付js接口所需的参数、生成获取共享收货地址所需的参数 * * 该类是微信支付提供的样例程序，商户可根据自己的需求修改，或者使用lib中的api自行开发 * * @author minkedong * */"""
def GetPayUrl(self, paydataobj):
"""生成直接支付url，支付url有效期为2小时 @param UnifiedOrderInput pa... | stack_v2_sparse_classes_10k_train_005221 | 1,899 | no_license | [
{
"docstring": "生成直接支付url，支付url有效期为2小时 @param UnifiedOrderInput paydataobj",
"name": "GetPayUrl",
"signature": "def GetPayUrl(self, paydataobj)"
},
{
"docstring": "/** * * 获取jsapi支付的参数 * @param array unifiedorderresult 统一支付接口返回的数据 * @throws WxPayException * * @return json数据，可直接填入js函数作为参数 */",
... | 2 | stack_v2_sparse_classes_30k_train_006867 | Implement the Python class `JsApiPay` described below.
Class description:
/** * * JSAPI支付实现类 * 该类实现了从微信公众平台获取code、通过code获取openid和access_token、 * 生成jsapi支付js接口所需的参数、生成获取共享收货地址所需的参数 * * 该类是微信支付提供的样例程序，商户可根据自己的需求修改，或者使用lib中的api自行开发 * * @author minkedong * */
Method signatures and docstrings:
- def GetPayUrl(self, paydat... | Implement the Python class `JsApiPay` described below.
Class description:
/** * * JSAPI支付实现类 * 该类实现了从微信公众平台获取code、通过code获取openid和access_token、 * 生成jsapi支付js接口所需的参数、生成获取共享收货地址所需的参数 * * 该类是微信支付提供的样例程序，商户可根据自己的需求修改，或者使用lib中的api自行开发 * * @author minkedong * */
Method signatures and docstrings:
- def GetPayUrl(self, paydat... | 007882f6fcdb85eaef7f40e3180d3c028189f981 | <|skeleton|>
class JsApiPay:
"""/** * * JSAPI支付实现类 * 该类实现了从微信公众平台获取code、通过code获取openid和access_token、 * 生成jsapi支付js接口所需的参数、生成获取共享收货地址所需的参数 * * 该类是微信支付提供的样例程序，商户可根据自己的需求修改，或者使用lib中的api自行开发 * * @author minkedong * */"""
def GetPayUrl(self, paydataobj):
"""生成直接支付url，支付url有效期为2小时 @param UnifiedOrderInput pa... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class JsApiPay:
"""/** * * JSAPI支付实现类 * 该类实现了从微信公众平台获取code、通过code获取openid和access_token、 * 生成jsapi支付js接口所需的参数、生成获取共享收货地址所需的参数 * * 该类是微信支付提供的样例程序，商户可根据自己的需求修改，或者使用lib中的api自行开发 * * @author minkedong * */"""
def GetPayUrl(self, paydataobj):
"""生成直接支付url，支付url有效期为2小时 @param UnifiedOrderInput paydataobj"""
... | the_stack_v2_python_sparse | vip_ticketing_server/wechatpay/wxpay_sdk/WxPayJsApiPay.py | fuguangbei/dev_vip | train | 0 |
a86b02581f06d22d5907fefdb2ff7bb64f911b59 | [
"self._pol1, self._pol2 = (pol1, pol2)\nself.deg = self._pol1.deg * self._pol2.deg\n_pol1, _pol2 = (self._pol1.pol[::-1], self._pol2.pol[::-1])\nself.pol = np.zeros((1,))\nfor i in range(pol1.deg + 1):\n self.pol = polyadd(self.pol, _pol1[i] * polypow(_pol2, i))\nself.pol = self.pol[::-1]",
"y = self._pol1.eva... | <|body_start_0|>
self._pol1, self._pol2 = (pol1, pol2)
self.deg = self._pol1.deg * self._pol2.deg
_pol1, _pol2 = (self._pol1.pol[::-1], self._pol2.pol[::-1])
self.pol = np.zeros((1,))
for i in range(pol1.deg + 1):
self.pol = polyadd(self.pol, _pol1[i] * polypow(_pol2,... | Create polynomial from composition of two others. | CompPol | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CompPol:
"""Create polynomial from composition of two others."""
def __init__(self, pol1, pol2):
"""Composes two polynomials (i.e., `pol1'(`pol2')) with distinct covariance matrices. Considering we have polynomials f(x) = \\sum_i a_i x^i, g(x) = \\sum_j b_j x^j, with variances \\sigm... | stack_v2_sparse_classes_10k_train_005222 | 35,535 | permissive | [
{
"docstring": "Composes two polynomials (i.e., `pol1'(`pol2')) with distinct covariance matrices. Considering we have polynomials f(x) = \\\\sum_i a_i x^i, g(x) = \\\\sum_j b_j x^j, with variances \\\\sigma_f and \\\\sigma_g when evaluated (see active_work.maths.Polynomial), we compute \\\\sigma_fg(x) = \\\\si... | 2 | stack_v2_sparse_classes_30k_train_000413 | Implement the Python class `CompPol` described below.
Class description:
Create polynomial from composition of two others.
Method signatures and docstrings:
- def __init__(self, pol1, pol2): Composes two polynomials (i.e., `pol1'(`pol2')) with distinct covariance matrices. Considering we have polynomials f(x) = \\sum... | Implement the Python class `CompPol` described below.
Class description:
Create polynomial from composition of two others.
Method signatures and docstrings:
- def __init__(self, pol1, pol2): Composes two polynomials (i.e., `pol1'(`pol2')) with distinct covariance matrices. Considering we have polynomials f(x) = \\sum... | 99107a0d4935296b673f67469c1e2bd258954b9b | <|skeleton|>
class CompPol:
"""Create polynomial from composition of two others."""
def __init__(self, pol1, pol2):
"""Composes two polynomials (i.e., `pol1'(`pol2')) with distinct covariance matrices. Considering we have polynomials f(x) = \\sum_i a_i x^i, g(x) = \\sum_j b_j x^j, with variances \\sigm... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class CompPol:
"""Create polynomial from composition of two others."""
def __init__(self, pol1, pol2):
"""Composes two polynomials (i.e., `pol1'(`pol2')) with distinct covariance matrices. Considering we have polynomials f(x) = \\sum_i a_i x^i, g(x) = \\sum_j b_j x^j, with variances \\sigma_f and \\sig... | the_stack_v2_python_sparse | maths.py | yketa/active_work | train | 1 |
bee0836d1a0e9050ff63b65281205a654027f71c | [
"lists = filter(lambda x: x is not None, lists)\nif not lists:\n return\nlength = len(lists)\nfactor = 2\nwhile length > 0:\n i = 0\n while True:\n try:\n lists[i] = self.mergeTwoLists(lists[i], lists[i + factor / 2])\n except IndexError:\n break\n i += factor\n ... | <|body_start_0|>
lists = filter(lambda x: x is not None, lists)
if not lists:
return
length = len(lists)
factor = 2
while length > 0:
i = 0
while True:
try:
lists[i] = self.mergeTwoLists(lists[i], lists[i + f... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def mergeKLists_TLE1(self, lists):
"""k lists; each list has N items Algorithm 1: Merge the lists 1 by 1, just add a loop outside the merge. Complexity: 2N+3N+4N+..kN = O(k^2 * N) Algorithm 2: Group the lists in pairs with every pair having 2 lists, merge two, then repeat again... | stack_v2_sparse_classes_10k_train_005223 | 3,498 | permissive | [
{
"docstring": "k lists; each list has N items Algorithm 1: Merge the lists 1 by 1, just add a loop outside the merge. Complexity: 2N+3N+4N+..kN = O(k^2 * N) Algorithm 2: Group the lists in pairs with every pair having 2 lists, merge two, then repeat again Complexity: T(N) = (k/2)*2N+(k/4)*4N..+(k/2^r)*2^r*N T(... | 4 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def mergeKLists_TLE1(self, lists): k lists; each list has N items Algorithm 1: Merge the lists 1 by 1, just add a loop outside the merge. Complexity: 2N+3N+4N+..kN = O(k^2 * N) A... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def mergeKLists_TLE1(self, lists): k lists; each list has N items Algorithm 1: Merge the lists 1 by 1, just add a loop outside the merge. Complexity: 2N+3N+4N+..kN = O(k^2 * N) A... | cbbd4a67ab342ada2421e13f82d660b1d47d4d20 | <|skeleton|>
class Solution:
def mergeKLists_TLE1(self, lists):
"""k lists; each list has N items Algorithm 1: Merge the lists 1 by 1, just add a loop outside the merge. Complexity: 2N+3N+4N+..kN = O(k^2 * N) Algorithm 2: Group the lists in pairs with every pair having 2 lists, merge two, then repeat again... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def mergeKLists_TLE1(self, lists):
"""k lists; each list has N items Algorithm 1: Merge the lists 1 by 1, just add a loop outside the merge. Complexity: 2N+3N+4N+..kN = O(k^2 * N) Algorithm 2: Group the lists in pairs with every pair having 2 lists, merge two, then repeat again Complexity: T... | the_stack_v2_python_sparse | 022 Merge k Sorted Lists.py | Aminaba123/LeetCode | train | 1 | |
69fdf7292ea892b1421982e198fa611bb973b4d1 | [
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')"
] | <|body_start_0|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
<|end_body_0|>
<|body_start_1|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not im... | A session represents an interaction with a user. You retrieve user input and pass it to the [DetectIntent][google.cloud.dialogflow.v2.Sessions.DetectIntent] (or [StreamingDetectIntent][google.cloud.dialogflow.v2.Sessions.StreamingDetectIntent]) method to determine user intent and respond. | SessionsServicer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SessionsServicer:
"""A session represents an interaction with a user. You retrieve user input and pass it to the [DetectIntent][google.cloud.dialogflow.v2.Sessions.DetectIntent] (or [StreamingDetectIntent][google.cloud.dialogflow.v2.Sessions.StreamingDetectIntent]) method to determine user intent... | stack_v2_sparse_classes_10k_train_005224 | 3,682 | permissive | [
{
"docstring": "Processes a natural language query and returns structured, actionable data as a result. This method is not idempotent, because it may cause contexts and session entity types to be updated, which in turn might affect results of future queries.",
"name": "DetectIntent",
"signature": "def D... | 2 | stack_v2_sparse_classes_30k_train_006671 | Implement the Python class `SessionsServicer` described below.
Class description:
A session represents an interaction with a user. You retrieve user input and pass it to the [DetectIntent][google.cloud.dialogflow.v2.Sessions.DetectIntent] (or [StreamingDetectIntent][google.cloud.dialogflow.v2.Sessions.StreamingDetectI... | Implement the Python class `SessionsServicer` described below.
Class description:
A session represents an interaction with a user. You retrieve user input and pass it to the [DetectIntent][google.cloud.dialogflow.v2.Sessions.DetectIntent] (or [StreamingDetectIntent][google.cloud.dialogflow.v2.Sessions.StreamingDetectI... | c9c830feb6b66c2e362f8fb5d147ef0c4f4a08cf | <|skeleton|>
class SessionsServicer:
"""A session represents an interaction with a user. You retrieve user input and pass it to the [DetectIntent][google.cloud.dialogflow.v2.Sessions.DetectIntent] (or [StreamingDetectIntent][google.cloud.dialogflow.v2.Sessions.StreamingDetectIntent]) method to determine user intent... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SessionsServicer:
"""A session represents an interaction with a user. You retrieve user input and pass it to the [DetectIntent][google.cloud.dialogflow.v2.Sessions.DetectIntent] (or [StreamingDetectIntent][google.cloud.dialogflow.v2.Sessions.StreamingDetectIntent]) method to determine user intent and respond.... | the_stack_v2_python_sparse | pyenv/lib/python3.6/site-packages/dialogflow_v2/proto/session_pb2_grpc.py | ronald-rgr/ai-chatbot-smartguide | train | 0 |
0d60b7b8526aa669ba65b13104a262556c82576a | [
"if not image_key:\n image_key = 'image/encoded'\nif not format_key:\n format_key = 'image/format'\nsuper(Image, self).__init__([image_key, format_key])\nself._image_key = image_key\nself._format_key = format_key\nself._shape = shape\nself._channels = channels\nself._dtype = dtype\nself._repeated = repeated",... | <|body_start_0|>
if not image_key:
image_key = 'image/encoded'
if not format_key:
format_key = 'image/format'
super(Image, self).__init__([image_key, format_key])
self._image_key = image_key
self._format_key = format_key
self._shape = shape
... | An ItemHandler that decodes a parsed Tensor as an image. | Image | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Image:
"""An ItemHandler that decodes a parsed Tensor as an image."""
def __init__(self, image_key=None, format_key=None, shape=None, channels=3, dtype=dtypes.uint8, repeated=False):
"""Initializes the image. Args: image_key: the name of the TF-Example feature in which the encoded im... | stack_v2_sparse_classes_10k_train_005225 | 15,383 | permissive | [
{
"docstring": "Initializes the image. Args: image_key: the name of the TF-Example feature in which the encoded image is stored. format_key: the name of the TF-Example feature in which the image format is stored. shape: the output shape of the image as 1-D `Tensor` [height, width, channels]. If provided, the im... | 3 | null | Implement the Python class `Image` described below.
Class description:
An ItemHandler that decodes a parsed Tensor as an image.
Method signatures and docstrings:
- def __init__(self, image_key=None, format_key=None, shape=None, channels=3, dtype=dtypes.uint8, repeated=False): Initializes the image. Args: image_key: t... | Implement the Python class `Image` described below.
Class description:
An ItemHandler that decodes a parsed Tensor as an image.
Method signatures and docstrings:
- def __init__(self, image_key=None, format_key=None, shape=None, channels=3, dtype=dtypes.uint8, repeated=False): Initializes the image. Args: image_key: t... | cabf6e4f1970dc14302f87414f170de19944bac2 | <|skeleton|>
class Image:
"""An ItemHandler that decodes a parsed Tensor as an image."""
def __init__(self, image_key=None, format_key=None, shape=None, channels=3, dtype=dtypes.uint8, repeated=False):
"""Initializes the image. Args: image_key: the name of the TF-Example feature in which the encoded im... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Image:
"""An ItemHandler that decodes a parsed Tensor as an image."""
def __init__(self, image_key=None, format_key=None, shape=None, channels=3, dtype=dtypes.uint8, repeated=False):
"""Initializes the image. Args: image_key: the name of the TF-Example feature in which the encoded image is stored... | the_stack_v2_python_sparse | Tensorflow_OpenCV_Nightly/source/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py | ryfeus/lambda-packs | train | 1,283 |
44ce5d5fa5634915f70dbf9aed7447697dcb25b1 | [
"sm = get_storage_manager()\naction_dict = rest_utils.get_json_and_verify_params({'action': {'type': str}})\nplugin = sm.get(models.Plugin, plugin_id)\nif action_dict.get('action') == 'install':\n install_dict = rest_utils.get_json_and_verify_params({'managers': {'type': list, 'optional': True}, 'agents': {'type... | <|body_start_0|>
sm = get_storage_manager()
action_dict = rest_utils.get_json_and_verify_params({'action': {'type': str}})
plugin = sm.get(models.Plugin, plugin_id)
if action_dict.get('action') == 'install':
install_dict = rest_utils.get_json_and_verify_params({'managers': {'... | PluginsId | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PluginsId:
def post(self, plugin_id, **kwargs):
"""Force plugin installation on the given managers or agents. This method is for internal use only."""
<|body_0|>
def put(self, plugin_id, **kwargs):
"""Update the plugin, specifically the installation state. Only updat... | stack_v2_sparse_classes_10k_train_005226 | 14,516 | permissive | [
{
"docstring": "Force plugin installation on the given managers or agents. This method is for internal use only.",
"name": "post",
"signature": "def post(self, plugin_id, **kwargs)"
},
{
"docstring": "Update the plugin, specifically the installation state. Only updating the state is supported ri... | 3 | null | Implement the Python class `PluginsId` described below.
Class description:
Implement the PluginsId class.
Method signatures and docstrings:
- def post(self, plugin_id, **kwargs): Force plugin installation on the given managers or agents. This method is for internal use only.
- def put(self, plugin_id, **kwargs): Upda... | Implement the Python class `PluginsId` described below.
Class description:
Implement the PluginsId class.
Method signatures and docstrings:
- def post(self, plugin_id, **kwargs): Force plugin installation on the given managers or agents. This method is for internal use only.
- def put(self, plugin_id, **kwargs): Upda... | c0de6442e1d7653fad824d75e571802a74eee605 | <|skeleton|>
class PluginsId:
def post(self, plugin_id, **kwargs):
"""Force plugin installation on the given managers or agents. This method is for internal use only."""
<|body_0|>
def put(self, plugin_id, **kwargs):
"""Update the plugin, specifically the installation state. Only updat... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PluginsId:
def post(self, plugin_id, **kwargs):
"""Force plugin installation on the given managers or agents. This method is for internal use only."""
sm = get_storage_manager()
action_dict = rest_utils.get_json_and_verify_params({'action': {'type': str}})
plugin = sm.get(model... | the_stack_v2_python_sparse | rest-service/manager_rest/rest/resources_v3_1/plugins.py | cloudify-cosmo/cloudify-manager | train | 146 | |
a94defafafac0185705096d8ca5fee70ecb04e9f | [
"self.capacity = capacity\nself.time = 0\nself.map = {}\nself.freq_time = {}\nself.priority_queue = []\nself.update = set()",
"self.time += 1\nif key in self.map:\n freq, _ = self.freq_time[key]\n self.freq_time[key] = (freq + 1, self.time)\n self.update.add(key)\n return self.map[key]\nreturn -1",
... | <|body_start_0|>
self.capacity = capacity
self.time = 0
self.map = {}
self.freq_time = {}
self.priority_queue = []
self.update = set()
<|end_body_0|>
<|body_start_1|>
self.time += 1
if key in self.map:
freq, _ = self.freq_time[key]
... | LFUCache | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LFUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, value):
""":type key: int :type value: int :rtype: void"""
<|body_2|>
<|end_s... | stack_v2_sparse_classes_10k_train_005227 | 3,017 | no_license | [
{
"docstring": ":type capacity: int",
"name": "__init__",
"signature": "def __init__(self, capacity)"
},
{
"docstring": ":type key: int :rtype: int",
"name": "get",
"signature": "def get(self, key)"
},
{
"docstring": ":type key: int :type value: int :rtype: void",
"name": "pu... | 3 | null | Implement the Python class `LFUCache` described below.
Class description:
Implement the LFUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: void | Implement the Python class `LFUCache` described below.
Class description:
Implement the LFUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: void
<|sk... | 05e0beff0047f0ad399d0b46d625bb8d3459814e | <|skeleton|>
class LFUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, value):
""":type key: int :type value: int :rtype: void"""
<|body_2|>
<|end_s... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class LFUCache:
def __init__(self, capacity):
""":type capacity: int"""
self.capacity = capacity
self.time = 0
self.map = {}
self.freq_time = {}
self.priority_queue = []
self.update = set()
def get(self, key):
""":type key: int :rtype: int"""
... | the_stack_v2_python_sparse | python_1_to_1000/460_LFU_Cache.py | jakehoare/leetcode | train | 58 | |
9b1b60a94c34ff4b295439abdff7378bfeabbe87 | [
"self.file_data = []\nself.header = {}\ntot_filepath = 'Data/WDCGG-SurfaceData/' + country + '/'\ntot_filepath += load_filename + '.dat'\nwith open(tot_filepath, 'rb') as file:\n for row in file:\n string_row = row.decode()\n if string_row[0] == 'C':\n try:\n key, value = ... | <|body_start_0|>
self.file_data = []
self.header = {}
tot_filepath = 'Data/WDCGG-SurfaceData/' + country + '/'
tot_filepath += load_filename + '.dat'
with open(tot_filepath, 'rb') as file:
for row in file:
string_row = row.decode()
if s... | Defines a child class of AtmosGasTS, with methods used to read in and parse the .dat files used by the WDCGG: http://ds.data.jma.go.jp/gmd/wdcgg/wdcgg.html | WDCGG_TS | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WDCGG_TS:
"""Defines a child class of AtmosGasTS, with methods used to read in and parse the .dat files used by the WDCGG: http://ds.data.jma.go.jp/gmd/wdcgg/wdcgg.html"""
def __init__(self, country, load_filename):
"""Initialises the class based on data found in the .dat files. coun... | stack_v2_sparse_classes_10k_train_005228 | 28,351 | no_license | [
{
"docstring": "Initialises the class based on data found in the .dat files. country -- string; the nation where the data came from, this will be used in filepaths and plot titles; so make sure the folders exit before run time. load_filename -- string; the file name/file path + file name where the .dat file is ... | 2 | stack_v2_sparse_classes_30k_train_004627 | Implement the Python class `WDCGG_TS` described below.
Class description:
Defines a child class of AtmosGasTS, with methods used to read in and parse the .dat files used by the WDCGG: http://ds.data.jma.go.jp/gmd/wdcgg/wdcgg.html
Method signatures and docstrings:
- def __init__(self, country, load_filename): Initiali... | Implement the Python class `WDCGG_TS` described below.
Class description:
Defines a child class of AtmosGasTS, with methods used to read in and parse the .dat files used by the WDCGG: http://ds.data.jma.go.jp/gmd/wdcgg/wdcgg.html
Method signatures and docstrings:
- def __init__(self, country, load_filename): Initiali... | 69c3beb334cb64b257c4496607a9b70dd220098b | <|skeleton|>
class WDCGG_TS:
"""Defines a child class of AtmosGasTS, with methods used to read in and parse the .dat files used by the WDCGG: http://ds.data.jma.go.jp/gmd/wdcgg/wdcgg.html"""
def __init__(self, country, load_filename):
"""Initialises the class based on data found in the .dat files. coun... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class WDCGG_TS:
"""Defines a child class of AtmosGasTS, with methods used to read in and parse the .dat files used by the WDCGG: http://ds.data.jma.go.jp/gmd/wdcgg/wdcgg.html"""
def __init__(self, country, load_filename):
"""Initialises the class based on data found in the .dat files. country -- string... | the_stack_v2_python_sparse | ScriptsMisc/AtmosGasTSclass (old).py | tmed2/Atmos2016 | train | 0 |
6100f1a09996674b67a958a7026ada368ae699fb | [
"nn.Module.__init__(self)\nself.reduction = reduction\nself.criterion = nn.MSELoss(reduction='none')",
"loss = self.criterion(input * mask, target * mask)\nif self.reduction == 'mean':\n loss = torch.sum(loss) / torch.sum(mask)\nreturn loss"
] | <|body_start_0|>
nn.Module.__init__(self)
self.reduction = reduction
self.criterion = nn.MSELoss(reduction='none')
<|end_body_0|>
<|body_start_1|>
loss = self.criterion(input * mask, target * mask)
if self.reduction == 'mean':
loss = torch.sum(loss) / torch.sum(mask)... | Compute the MSE loss only on the masked region. | MaskedMSELoss | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MaskedMSELoss:
"""Compute the MSE loss only on the masked region."""
def __init__(self, reduction='mean'):
"""Loss Constructor. ---------- INPUT |---- reduction (str) the reduction to use on the loss. ONLY 'mean' or 'none'. OUTPUT |---- None"""
<|body_0|>
def forward(sel... | stack_v2_sparse_classes_10k_train_005229 | 18,386 | permissive | [
{
"docstring": "Loss Constructor. ---------- INPUT |---- reduction (str) the reduction to use on the loss. ONLY 'mean' or 'none'. OUTPUT |---- None",
"name": "__init__",
"signature": "def __init__(self, reduction='mean')"
},
{
"docstring": "Forard pass of the loss. The loss is computed only wher... | 2 | stack_v2_sparse_classes_30k_train_004423 | Implement the Python class `MaskedMSELoss` described below.
Class description:
Compute the MSE loss only on the masked region.
Method signatures and docstrings:
- def __init__(self, reduction='mean'): Loss Constructor. ---------- INPUT |---- reduction (str) the reduction to use on the loss. ONLY 'mean' or 'none'. OUT... | Implement the Python class `MaskedMSELoss` described below.
Class description:
Compute the MSE loss only on the masked region.
Method signatures and docstrings:
- def __init__(self, reduction='mean'): Loss Constructor. ---------- INPUT |---- reduction (str) the reduction to use on the loss. ONLY 'mean' or 'none'. OUT... | 850b6195d6290a50eee865b4d5a66f5db5260e8f | <|skeleton|>
class MaskedMSELoss:
"""Compute the MSE loss only on the masked region."""
def __init__(self, reduction='mean'):
"""Loss Constructor. ---------- INPUT |---- reduction (str) the reduction to use on the loss. ONLY 'mean' or 'none'. OUTPUT |---- None"""
<|body_0|>
def forward(sel... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MaskedMSELoss:
"""Compute the MSE loss only on the masked region."""
def __init__(self, reduction='mean'):
"""Loss Constructor. ---------- INPUT |---- reduction (str) the reduction to use on the loss. ONLY 'mean' or 'none'. OUTPUT |---- None"""
nn.Module.__init__(self)
self.reduct... | the_stack_v2_python_sparse | Code/src/models/optim/CustomLosses.py | antoine-spahr/X-ray-Anomaly-Detection | train | 3 |
50b676de636bbed5272c4c0fea2f790feea5fc4c | [
"t = self.observation['t']\nx, y = model.get_x_y(t)\nreturn {'t': t, 'x': x, 'y': y}",
"delta_x = observation['x'] - prediction['x']\ndelta_y = observation['y'] - prediction['y']\nerror = np.sqrt(delta_x ** 2 + delta_y ** 2)\npassing = bool(error < 100000.0 * pq.kilometer)\nscore = self.score_type(passing)\nscore... | <|body_start_0|>
t = self.observation['t']
x, y = model.get_x_y(t)
return {'t': t, 'x': x, 'y': y}
<|end_body_0|>
<|body_start_1|>
delta_x = observation['x'] - prediction['x']
delta_y = observation['y'] - prediction['y']
error = np.sqrt(delta_x ** 2 + delta_y ** 2)
... | A test of a planetary position at some specified time. | PositionTest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PositionTest:
"""A test of a planetary position at some specified time."""
def generate_prediction(self, model):
"""Generate a prediction from a model."""
<|body_0|>
def compute_score(self, observation, prediction):
"""Compute a test score based on the agreement ... | stack_v2_sparse_classes_10k_train_005230 | 4,646 | no_license | [
{
"docstring": "Generate a prediction from a model.",
"name": "generate_prediction",
"signature": "def generate_prediction(self, model)"
},
{
"docstring": "Compute a test score based on the agreement between the observation (data) and prediction (model).",
"name": "compute_score",
"signa... | 2 | null | Implement the Python class `PositionTest` described below.
Class description:
A test of a planetary position at some specified time.
Method signatures and docstrings:
- def generate_prediction(self, model): Generate a prediction from a model.
- def compute_score(self, observation, prediction): Compute a test score ba... | Implement the Python class `PositionTest` described below.
Class description:
A test of a planetary position at some specified time.
Method signatures and docstrings:
- def generate_prediction(self, model): Generate a prediction from a model.
- def compute_score(self, observation, prediction): Compute a test score ba... | 624bf82ce5c610c2ca83a0c4c49d3f4d0b92a1e2 | <|skeleton|>
class PositionTest:
"""A test of a planetary position at some specified time."""
def generate_prediction(self, model):
"""Generate a prediction from a model."""
<|body_0|>
def compute_score(self, observation, prediction):
"""Compute a test score based on the agreement ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PositionTest:
"""A test of a planetary position at some specified time."""
def generate_prediction(self, model):
"""Generate a prediction from a model."""
t = self.observation['t']
x, y = model.get_x_y(t)
return {'t': t, 'x': x, 'y': y}
def compute_score(self, observa... | the_stack_v2_python_sparse | unittest/sciunittest.py | HussainAther/neuroscience | train | 9 |
af1dade54e8aa2d4cc19216a1896cb7dd9184dfd | [
"version = pcs.Field('version', 4, default=4)\nhlen = pcs.Field('hlen', 4)\ntos = pcs.Field('tos', 8)\nlength = pcs.Field('length', 16)\nid = pcs.Field('id', 16)\nflags = pcs.Field('flags', 3)\noffset = pcs.Field('offset', 13)\nttl = pcs.Field('ttl', 8, default=64)\nprotocol = pcs.Field('protocol', 8)\nchecksum = p... | <|body_start_0|>
version = pcs.Field('version', 4, default=4)
hlen = pcs.Field('hlen', 4)
tos = pcs.Field('tos', 8)
length = pcs.Field('length', 16)
id = pcs.Field('id', 16)
flags = pcs.Field('flags', 3)
offset = pcs.Field('offset', 13)
ttl = pcs.Field('tt... | ipv4 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ipv4:
def __init__(self, bytes=None):
"""define the fields of an IPv4 packet, from RFC 791 This version does not include options."""
<|body_0|>
def __str__(self):
"""Walk the entire packet and pretty print the values of the fields."""
<|body_1|>
def next... | stack_v2_sparse_classes_10k_train_005231 | 5,722 | no_license | [
{
"docstring": "define the fields of an IPv4 packet, from RFC 791 This version does not include options.",
"name": "__init__",
"signature": "def __init__(self, bytes=None)"
},
{
"docstring": "Walk the entire packet and pretty print the values of the fields.",
"name": "__str__",
"signatur... | 4 | stack_v2_sparse_classes_30k_train_000337 | Implement the Python class `ipv4` described below.
Class description:
Implement the ipv4 class.
Method signatures and docstrings:
- def __init__(self, bytes=None): define the fields of an IPv4 packet, from RFC 791 This version does not include options.
- def __str__(self): Walk the entire packet and pretty print the ... | Implement the Python class `ipv4` described below.
Class description:
Implement the ipv4 class.
Method signatures and docstrings:
- def __init__(self, bytes=None): define the fields of an IPv4 packet, from RFC 791 This version does not include options.
- def __str__(self): Walk the entire packet and pretty print the ... | a070a39586b582fbeea72abf12bbfd812955ad81 | <|skeleton|>
class ipv4:
def __init__(self, bytes=None):
"""define the fields of an IPv4 packet, from RFC 791 This version does not include options."""
<|body_0|>
def __str__(self):
"""Walk the entire packet and pretty print the values of the fields."""
<|body_1|>
def next... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ipv4:
def __init__(self, bytes=None):
"""define the fields of an IPv4 packet, from RFC 791 This version does not include options."""
version = pcs.Field('version', 4, default=4)
hlen = pcs.Field('hlen', 4)
tos = pcs.Field('tos', 8)
length = pcs.Field('length', 16)
... | the_stack_v2_python_sparse | src/pcs/packets/ipv4.py | bilouro/tcptest | train | 0 | |
93a7c2a9db6cdc0847db634aad809ecc038fe1ec | [
"super().__init__(base_url=base_url, proxy=proxy, verify=verify)\nself.api_key = api_key\nif self.api_key:\n self._headers = {'Key': self.api_key}",
"request_params: Dict[str, Any] = {}\nif offset:\n request_params['offset'] = offset\nif max_results:\n request_params['limit'] = max_results\nif start_time... | <|body_start_0|>
super().__init__(base_url=base_url, proxy=proxy, verify=verify)
self.api_key = api_key
if self.api_key:
self._headers = {'Key': self.api_key}
<|end_body_0|>
<|body_start_1|>
request_params: Dict[str, Any] = {}
if offset:
request_params['o... | This class is responsible for dealing with the XDR api. It performs all the fetching related commands andensure proper pre processing ebfore forward events. | Client | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Client:
"""This class is responsible for dealing with the XDR api. It performs all the fetching related commands andensure proper pre processing ebfore forward events."""
def __init__(self, api_key: Optional[str], base_url: Optional[str], proxy: Optional[bool], verify: Optional[bool]):
... | stack_v2_sparse_classes_10k_train_005232 | 14,388 | permissive | [
{
"docstring": "This function initializes the connection with the API server by collecting curcial information from the users.",
"name": "__init__",
"signature": "def __init__(self, api_key: Optional[str], base_url: Optional[str], proxy: Optional[bool], verify: Optional[bool])"
},
{
"docstring":... | 4 | null | Implement the Python class `Client` described below.
Class description:
This class is responsible for dealing with the XDR api. It performs all the fetching related commands andensure proper pre processing ebfore forward events.
Method signatures and docstrings:
- def __init__(self, api_key: Optional[str], base_url: ... | Implement the Python class `Client` described below.
Class description:
This class is responsible for dealing with the XDR api. It performs all the fetching related commands andensure proper pre processing ebfore forward events.
Method signatures and docstrings:
- def __init__(self, api_key: Optional[str], base_url: ... | 890def5a0e0ae8d6eaa538148249ddbc851dbb6b | <|skeleton|>
class Client:
"""This class is responsible for dealing with the XDR api. It performs all the fetching related commands andensure proper pre processing ebfore forward events."""
def __init__(self, api_key: Optional[str], base_url: Optional[str], proxy: Optional[bool], verify: Optional[bool]):
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Client:
"""This class is responsible for dealing with the XDR api. It performs all the fetching related commands andensure proper pre processing ebfore forward events."""
def __init__(self, api_key: Optional[str], base_url: Optional[str], proxy: Optional[bool], verify: Optional[bool]):
"""This fu... | the_stack_v2_python_sparse | Packs/Zerohack_XDR/Integrations/ZerohackXDR/ZerohackXDR.py | demisto/content | train | 1,023 |
36d68f5a360e7ee68991dafdd2b4b270e30a3e5b | [
"super().__init__(*args, **kwargs)\nself.value_shape = value_shape or ()\nself.num_values = int(np.prod(self.value_shape))\nself.iblt_values_shape = (self.repetitions, self.table_size) + self.value_shape\nif len(self.value_shape) > 1:\n self._tile_shape = [1] + [1 for _ in self.value_shape]\n self._tile_shape... | <|body_start_0|>
super().__init__(*args, **kwargs)
self.value_shape = value_shape or ()
self.num_values = int(np.prod(self.value_shape))
self.iblt_values_shape = (self.repetitions, self.table_size) + self.value_shape
if len(self.value_shape) > 1:
self._tile_shape = [1... | Encodes the strings into an IBLT data structure. | IbltTensorEncoder | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IbltTensorEncoder:
"""Encodes the strings into an IBLT data structure."""
def __init__(self, value_shape: Sequence[int], *args, **kwargs):
"""Initializes internal IBLT parameters. Args: value_shape: Shape of the values. *args: See IbltEncoder. **kwargs: See IbltEncoder."""
<|... | stack_v2_sparse_classes_10k_train_005233 | 17,202 | permissive | [
{
"docstring": "Initializes internal IBLT parameters. Args: value_shape: Shape of the values. *args: See IbltEncoder. **kwargs: See IbltEncoder.",
"name": "__init__",
"signature": "def __init__(self, value_shape: Sequence[int], *args, **kwargs)"
},
{
"docstring": "Returns SparseTensor with tenso... | 3 | stack_v2_sparse_classes_30k_train_006457 | Implement the Python class `IbltTensorEncoder` described below.
Class description:
Encodes the strings into an IBLT data structure.
Method signatures and docstrings:
- def __init__(self, value_shape: Sequence[int], *args, **kwargs): Initializes internal IBLT parameters. Args: value_shape: Shape of the values. *args: ... | Implement the Python class `IbltTensorEncoder` described below.
Class description:
Encodes the strings into an IBLT data structure.
Method signatures and docstrings:
- def __init__(self, value_shape: Sequence[int], *args, **kwargs): Initializes internal IBLT parameters. Args: value_shape: Shape of the values. *args: ... | ad4bca66f4b483e09d8396e9948630813a343d27 | <|skeleton|>
class IbltTensorEncoder:
"""Encodes the strings into an IBLT data structure."""
def __init__(self, value_shape: Sequence[int], *args, **kwargs):
"""Initializes internal IBLT parameters. Args: value_shape: Shape of the values. *args: See IbltEncoder. **kwargs: See IbltEncoder."""
<|... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class IbltTensorEncoder:
"""Encodes the strings into an IBLT data structure."""
def __init__(self, value_shape: Sequence[int], *args, **kwargs):
"""Initializes internal IBLT parameters. Args: value_shape: Shape of the values. *args: See IbltEncoder. **kwargs: See IbltEncoder."""
super().__init_... | the_stack_v2_python_sparse | tensorflow_federated/python/analytics/heavy_hitters/iblt/iblt_tensor.py | tensorflow/federated | train | 2,297 |
178287d23c96c09c9a2d4c68d6f4547ab7cadaee | [
"magnitudes, edges = np.histogram(data, bins)\nbin_width = edges[1] - edges[0]\nbin_sizes = magnitudes.astype(np.float) / (magnitudes.sum() * resolution)\nvalid_indices = np.where(bin_sizes >= 1)[0]\nif valid_indices.size == 0:\n raise ValueError('Resolution is too low. Cumulative distribution array is empty.')\... | <|body_start_0|>
magnitudes, edges = np.histogram(data, bins)
bin_width = edges[1] - edges[0]
bin_sizes = magnitudes.astype(np.float) / (magnitudes.sum() * resolution)
valid_indices = np.where(bin_sizes >= 1)[0]
if valid_indices.size == 0:
raise ValueError('Resolution... | Random number generator based on a modelled distribution. Given repeated observations of a single random variable, this object first models the probability distribution that governs the variable using a histogram. It then generates new variates according to this distribution. This sampler trades space for time by appro... | HistogramSampler | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HistogramSampler:
"""Random number generator based on a modelled distribution. Given repeated observations of a single random variable, this object first models the probability distribution that governs the variable using a histogram. It then generates new variates according to this distribution.... | stack_v2_sparse_classes_10k_train_005234 | 5,295 | permissive | [
{
"docstring": "Construct a new sampler object. :param data: Observations for a single random variable. :type data: 1D ndarray :param bins: Number of bins to use when generating the histogram. :type bins: positive int :param resolution: Resolution of each element of the cum-dist array. For example, a resolution... | 2 | stack_v2_sparse_classes_30k_train_005860 | Implement the Python class `HistogramSampler` described below.
Class description:
Random number generator based on a modelled distribution. Given repeated observations of a single random variable, this object first models the probability distribution that governs the variable using a histogram. It then generates new v... | Implement the Python class `HistogramSampler` described below.
Class description:
Random number generator based on a modelled distribution. Given repeated observations of a single random variable, this object first models the probability distribution that governs the variable using a histogram. It then generates new v... | 8b98390850351385acfda5be3088cd4db4cc4a09 | <|skeleton|>
class HistogramSampler:
"""Random number generator based on a modelled distribution. Given repeated observations of a single random variable, this object first models the probability distribution that governs the variable using a histogram. It then generates new variates according to this distribution.... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class HistogramSampler:
"""Random number generator based on a modelled distribution. Given repeated observations of a single random variable, this object first models the probability distribution that governs the variable using a histogram. It then generates new variates according to this distribution. This sampler... | the_stack_v2_python_sparse | glimpse/util/grandom.py | mthomure/glimpse-project | train | 1 |
c7f0a39910e06dd77e74f07b26f5e4fa5ac9e1d3 | [
"super(Annotation, self).__init__(*args, **kwargs)\nif not TOKEN:\n raise exceptions.InvalidCredentials('Missing the \"LIBRATO_TOKEN\" environment variable.')\nif not EMAIL:\n raise exceptions.InvalidCredentials('Missing the \"LIBRATO_EMAIL\" environment variable.')",
"try:\n res = (yield self._fetch(*ar... | <|body_start_0|>
super(Annotation, self).__init__(*args, **kwargs)
if not TOKEN:
raise exceptions.InvalidCredentials('Missing the "LIBRATO_TOKEN" environment variable.')
if not EMAIL:
raise exceptions.InvalidCredentials('Missing the "LIBRATO_EMAIL" environment variable.')... | Librato Annotation Actor Posts an Annotation to Librato. **Options** :title: The title of the annotation :description: The description of the annotation :name: Name of the metric to annotate **Examples** .. code-block:: json { "actor": "librato.Annotation", "desc": "Mark our deployment", "options": { "title": "Deploy",... | Annotation | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Annotation:
"""Librato Annotation Actor Posts an Annotation to Librato. **Options** :title: The title of the annotation :description: The description of the annotation :name: Name of the metric to annotate **Examples** .. code-block:: json { "actor": "librato.Annotation", "desc": "Mark our deploy... | stack_v2_sparse_classes_10k_train_005235 | 5,119 | permissive | [
{
"docstring": "Check for the needed environment variables.",
"name": "__init__",
"signature": "def __init__(self, *args, **kwargs)"
},
{
"docstring": "Wrap the superclass _fetch method to catch known Librato errors.",
"name": "_fetch_wrapper",
"signature": "def _fetch_wrapper(self, *arg... | 3 | stack_v2_sparse_classes_30k_test_000335 | Implement the Python class `Annotation` described below.
Class description:
Librato Annotation Actor Posts an Annotation to Librato. **Options** :title: The title of the annotation :description: The description of the annotation :name: Name of the metric to annotate **Examples** .. code-block:: json { "actor": "librat... | Implement the Python class `Annotation` described below.
Class description:
Librato Annotation Actor Posts an Annotation to Librato. **Options** :title: The title of the annotation :description: The description of the annotation :name: Name of the metric to annotate **Examples** .. code-block:: json { "actor": "librat... | d0abaf93ff321f12c0504c99eacb89f9288e892b | <|skeleton|>
class Annotation:
"""Librato Annotation Actor Posts an Annotation to Librato. **Options** :title: The title of the annotation :description: The description of the annotation :name: Name of the metric to annotate **Examples** .. code-block:: json { "actor": "librato.Annotation", "desc": "Mark our deploy... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Annotation:
"""Librato Annotation Actor Posts an Annotation to Librato. **Options** :title: The title of the annotation :description: The description of the annotation :name: Name of the metric to annotate **Examples** .. code-block:: json { "actor": "librato.Annotation", "desc": "Mark our deployment", "optio... | the_stack_v2_python_sparse | kingpin/actors/librato.py | Nextdoor/kingpin | train | 29 |
40873b9e0ffaadb6451f553b15a061d4381f80da | [
"if not isinstance(filePath, str):\n raise Exceptions.IncorrectTypeException(filePath, 'path', (str,))\nsuper().__init__(currentVersion, hostNamespace=hostNamespace)\nself.FilePath = filePath",
"operationSuccess = True\npersistentDataContainerString = '{}'\nif os.path.exists(self.FilePath):\n try:\n ... | <|body_start_0|>
if not isinstance(filePath, str):
raise Exceptions.IncorrectTypeException(filePath, 'path', (str,))
super().__init__(currentVersion, hostNamespace=hostNamespace)
self.FilePath = filePath
<|end_body_0|>
<|body_start_1|>
operationSuccess = True
persist... | A class for handling persistent data. This version will read and write the data to a file through the load and save methods. | PersistentBranchedFile | [
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PersistentBranchedFile:
"""A class for handling persistent data. This version will read and write the data to a file through the load and save methods."""
def __init__(self, filePath: str, currentVersion: Version.Version, hostNamespace: str=This.Mod.Namespace):
""":param filePath: Th... | stack_v2_sparse_classes_10k_train_005236 | 34,547 | permissive | [
{
"docstring": ":param filePath: The file path this persistence object will be written to and read from. :type filePath: str :param currentVersion: The current version of what ever will be controlling this persistence object. This value can allow you to correct outdated persistent data. :type currentVersion: Ve... | 3 | stack_v2_sparse_classes_30k_train_005178 | Implement the Python class `PersistentBranchedFile` described below.
Class description:
A class for handling persistent data. This version will read and write the data to a file through the load and save methods.
Method signatures and docstrings:
- def __init__(self, filePath: str, currentVersion: Version.Version, ho... | Implement the Python class `PersistentBranchedFile` described below.
Class description:
A class for handling persistent data. This version will read and write the data to a file through the load and save methods.
Method signatures and docstrings:
- def __init__(self, filePath: str, currentVersion: Version.Version, ho... | 2d85e6d4428f01294d2d34f1807287b753f7490c | <|skeleton|>
class PersistentBranchedFile:
"""A class for handling persistent data. This version will read and write the data to a file through the load and save methods."""
def __init__(self, filePath: str, currentVersion: Version.Version, hostNamespace: str=This.Mod.Namespace):
""":param filePath: Th... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PersistentBranchedFile:
"""A class for handling persistent data. This version will read and write the data to a file through the load and save methods."""
def __init__(self, filePath: str, currentVersion: Version.Version, hostNamespace: str=This.Mod.Namespace):
""":param filePath: The file path t... | the_stack_v2_python_sparse | Python/NeonOcean.S4.Main/NeonOcean/S4/Main/Data/PersistenceBranched.py | NeonOcean/S4.Main | train | 1 |
f6a9da15cd7d656815adf5d4625f848a44487e39 | [
"if la:\n return la.size() ** 2 + la[0]\nelse:\n return 0",
"if sexpr == 0:\n return self(0)\nif sexpr.support() == [[]]:\n return self._from_dict({self.one_basis(): sexpr.coefficient([])}, remove_zeros=False)\nout = self.zero()\nwhile sexpr:\n mup = max(sexpr.support(), key=self._my_key)\n out ... | <|body_start_0|>
if la:
return la.size() ** 2 + la[0]
else:
return 0
<|end_body_0|>
<|body_start_1|>
if sexpr == 0:
return self(0)
if sexpr.support() == [[]]:
return self._from_dict({self.one_basis(): sexpr.coefficient([])}, remove_zeros=F... | generic_character | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class generic_character:
def _my_key(self, la):
"""A rank function for partitions. The leading term of a homogeneous expression will be the partition with the largest key value. This key value is `|\\lambda|^2 + \\lambda_0` and using the ``max`` function on a list of Partitions. Of course it i... | stack_v2_sparse_classes_10k_train_005237 | 16,482 | no_license | [
{
"docstring": "A rank function for partitions. The leading term of a homogeneous expression will be the partition with the largest key value. This key value is `|\\\\lambda|^2 + \\\\lambda_0` and using the ``max`` function on a list of Partitions. Of course it is possible that this rank function is equal for s... | 2 | stack_v2_sparse_classes_30k_train_005192 | Implement the Python class `generic_character` described below.
Class description:
Implement the generic_character class.
Method signatures and docstrings:
- def _my_key(self, la): A rank function for partitions. The leading term of a homogeneous expression will be the partition with the largest key value. This key v... | Implement the Python class `generic_character` described below.
Class description:
Implement the generic_character class.
Method signatures and docstrings:
- def _my_key(self, la): A rank function for partitions. The leading term of a homogeneous expression will be the partition with the largest key value. This key v... | 0d9eacbf74e2acffefde93e39f8bcbec745cdaba | <|skeleton|>
class generic_character:
def _my_key(self, la):
"""A rank function for partitions. The leading term of a homogeneous expression will be the partition with the largest key value. This key value is `|\\lambda|^2 + \\lambda_0` and using the ``max`` function on a list of Partitions. Of course it i... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class generic_character:
def _my_key(self, la):
"""A rank function for partitions. The leading term of a homogeneous expression will be the partition with the largest key value. This key value is `|\\lambda|^2 + \\lambda_0` and using the ``max`` function on a list of Partitions. Of course it is possible tha... | the_stack_v2_python_sparse | sage/src/sage/combinat/sf/character.py | bopopescu/geosci | train | 0 | |
731e2a1ec51dd25ff6b080b779eb0578ce1f8ad9 | [
"hour = 0\nfor count in piles:\n hour += count / k\n if count % k != 0:\n hour += 1\nreturn hour",
"if not piles:\n return 0\nleft = 1\nright = max(piles)\nwhile left + 1 < right:\n middle = (left + right) / 2\n hour = self.calHour(middle, piles)\n if hour == H:\n right = middle\n ... | <|body_start_0|>
hour = 0
for count in piles:
hour += count / k
if count % k != 0:
hour += 1
return hour
<|end_body_0|>
<|body_start_1|>
if not piles:
return 0
left = 1
right = max(piles)
while left + 1 < right:... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def calHour(self, k, piles):
"""calculate how many hours koko takes eating up all piles of bananas"""
<|body_0|>
def minEatingSpeed(self, piles, H):
""":type piles: List[int] :type H: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>... | stack_v2_sparse_classes_10k_train_005238 | 1,004 | no_license | [
{
"docstring": "calculate how many hours koko takes eating up all piles of bananas",
"name": "calHour",
"signature": "def calHour(self, k, piles)"
},
{
"docstring": ":type piles: List[int] :type H: int :rtype: int",
"name": "minEatingSpeed",
"signature": "def minEatingSpeed(self, piles, ... | 2 | stack_v2_sparse_classes_30k_test_000119 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def calHour(self, k, piles): calculate how many hours koko takes eating up all piles of bananas
- def minEatingSpeed(self, piles, H): :type piles: List[int] :type H: int :rtype: ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def calHour(self, k, piles): calculate how many hours koko takes eating up all piles of bananas
- def minEatingSpeed(self, piles, H): :type piles: List[int] :type H: int :rtype: ... | 1d8821da01c9c200732a6b7037b8631689e2f7e7 | <|skeleton|>
class Solution:
def calHour(self, k, piles):
"""calculate how many hours koko takes eating up all piles of bananas"""
<|body_0|>
def minEatingSpeed(self, piles, H):
""":type piles: List[int] :type H: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def calHour(self, k, piles):
"""calculate how many hours koko takes eating up all piles of bananas"""
hour = 0
for count in piles:
hour += count / k
if count % k != 0:
hour += 1
return hour
def minEatingSpeed(self, piles, H... | the_stack_v2_python_sparse | Leetcode0875_BinarySearch.py | xiaojinghu/Leetcode | train | 0 | |
248b4261ea8199e77a5478eafe17e8e2521894c4 | [
"self._modules = args\nself._base_module = kwargs.get('base_module', 'galileo')\nif not self._modules:\n self._modules = [self._base_module]",
"for module in self._modules:\n m = sys.modules[module]\n setattr(m, func.__name__, func)\nreturn func",
"for module in self._modules:\n m = sys.modules[modu... | <|body_start_0|>
self._modules = args
self._base_module = kwargs.get('base_module', 'galileo')
if not self._modules:
self._modules = [self._base_module]
<|end_body_0|>
<|body_start_1|>
for module in self._modules:
m = sys.modules[module]
setattr(m, fu... | \\brief Export galileo APIs | export | [
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0",
"BSD-2-Clause",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class export:
"""\\brief Export galileo APIs"""
def __init__(self, *args, **kwargs):
"""\\param *args modules, expected modules, dot delimited format. Default use base_module, e.g. galileo or galileo.tf \\param **kwargs base_module Default is `galileo`."""
<|body_0|>
def __cal... | stack_v2_sparse_classes_10k_train_005239 | 2,131 | permissive | [
{
"docstring": "\\\\param *args modules, expected modules, dot delimited format. Default use base_module, e.g. galileo or galileo.tf \\\\param **kwargs base_module Default is `galileo`.",
"name": "__init__",
"signature": "def __init__(self, *args, **kwargs)"
},
{
"docstring": "\\\\brief export c... | 4 | stack_v2_sparse_classes_30k_test_000184 | Implement the Python class `export` described below.
Class description:
\\brief Export galileo APIs
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): \\param *args modules, expected modules, dot delimited format. Default use base_module, e.g. galileo or galileo.tf \\param **kwargs base_module D... | Implement the Python class `export` described below.
Class description:
\\brief Export galileo APIs
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): \\param *args modules, expected modules, dot delimited format. Default use base_module, e.g. galileo or galileo.tf \\param **kwargs base_module D... | 48099ec3f0331196c6812208ceb080ba618a588b | <|skeleton|>
class export:
"""\\brief Export galileo APIs"""
def __init__(self, *args, **kwargs):
"""\\param *args modules, expected modules, dot delimited format. Default use base_module, e.g. galileo or galileo.tf \\param **kwargs base_module Default is `galileo`."""
<|body_0|>
def __cal... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class export:
"""\\brief Export galileo APIs"""
def __init__(self, *args, **kwargs):
"""\\param *args modules, expected modules, dot delimited format. Default use base_module, e.g. galileo or galileo.tf \\param **kwargs base_module Default is `galileo`."""
self._modules = args
self._bas... | the_stack_v2_python_sparse | galileo/platform/export.py | 2012fang1/galileo | train | 0 |
59b53af55bab5cc560fcb2243f77a5802002be72 | [
"preorder, inorder = ([], [])\n\ndef helper(root):\n if not root:\n return\n preorder.append(root.val)\n helper(root.left)\n inorder.append(root.val)\n helper(root.right)\nhelper(root)\nreturn ':'.join(map(str, preorder)) + ':' + ':'.join(map(str, inorder))",
"l = data.split(':')\nif l == ['... | <|body_start_0|>
preorder, inorder = ([], [])
def helper(root):
if not root:
return
preorder.append(root.val)
helper(root.left)
inorder.append(root.val)
helper(root.right)
helper(root)
return ':'.join(map(str, p... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_10k_train_005240 | 1,568 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | stack_v2_sparse_classes_30k_train_001857 | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | 4ef763841632f2ba0a616b13c70e8650ada4ae16 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
preorder, inorder = ([], [])
def helper(root):
if not root:
return
preorder.append(root.val)
helper(root.left)
in... | the_stack_v2_python_sparse | leetcode449.py | kduan005/Leetcode | train | 0 | |
c32362a237de3cc389f85c7ae5e004c2bdb102fb | [
"super(mesh_to_mesh_petsc_dmda, self).__init__(fine_prob, coarse_prob, params)\nself.interp, _ = self.coarse_prob.init.createInterpolation(self.fine_prob.init)\nself.inject = self.coarse_prob.init.createInjection(self.fine_prob.init)",
"if isinstance(F, petsc_vec):\n u_coarse = self.coarse_prob.dtype_u(self.co... | <|body_start_0|>
super(mesh_to_mesh_petsc_dmda, self).__init__(fine_prob, coarse_prob, params)
self.interp, _ = self.coarse_prob.init.createInterpolation(self.fine_prob.init)
self.inject = self.coarse_prob.init.createInjection(self.fine_prob.init)
<|end_body_0|>
<|body_start_1|>
if isin... | This implementation can restrict and prolong between PETSc DMDA grids | mesh_to_mesh_petsc_dmda | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class mesh_to_mesh_petsc_dmda:
"""This implementation can restrict and prolong between PETSc DMDA grids"""
def __init__(self, fine_prob, coarse_prob, params):
"""Initialization routine Args: fine_prob: fine problem coarse_prob: coarse problem params: parameters for the transfer operators""... | stack_v2_sparse_classes_10k_train_005241 | 2,871 | permissive | [
{
"docstring": "Initialization routine Args: fine_prob: fine problem coarse_prob: coarse problem params: parameters for the transfer operators",
"name": "__init__",
"signature": "def __init__(self, fine_prob, coarse_prob, params)"
},
{
"docstring": "Restriction implementation Args: F: the fine l... | 3 | null | Implement the Python class `mesh_to_mesh_petsc_dmda` described below.
Class description:
This implementation can restrict and prolong between PETSc DMDA grids
Method signatures and docstrings:
- def __init__(self, fine_prob, coarse_prob, params): Initialization routine Args: fine_prob: fine problem coarse_prob: coars... | Implement the Python class `mesh_to_mesh_petsc_dmda` described below.
Class description:
This implementation can restrict and prolong between PETSc DMDA grids
Method signatures and docstrings:
- def __init__(self, fine_prob, coarse_prob, params): Initialization routine Args: fine_prob: fine problem coarse_prob: coars... | 1a51834bedffd4472e344bed28f4d766614b1537 | <|skeleton|>
class mesh_to_mesh_petsc_dmda:
"""This implementation can restrict and prolong between PETSc DMDA grids"""
def __init__(self, fine_prob, coarse_prob, params):
"""Initialization routine Args: fine_prob: fine problem coarse_prob: coarse problem params: parameters for the transfer operators""... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class mesh_to_mesh_petsc_dmda:
"""This implementation can restrict and prolong between PETSc DMDA grids"""
def __init__(self, fine_prob, coarse_prob, params):
"""Initialization routine Args: fine_prob: fine problem coarse_prob: coarse problem params: parameters for the transfer operators"""
sup... | the_stack_v2_python_sparse | pySDC/implementations/transfer_classes/TransferPETScDMDA.py | Parallel-in-Time/pySDC | train | 30 |
3ac688259da227071652034e0acaf2acf1f7fc31 | [
"self.capacity = capacity\nself.map = {}\nself.cache = LinkedList()",
"if key in self.map:\n node = self.map[key]\n self.cache.remove(node)\n self.cache.append(node)\n return node.value\nreturn -1",
"if key not in self.map:\n if len(self.cache) == self.capacity:\n node = self.cache.pop()\n... | <|body_start_0|>
self.capacity = capacity
self.map = {}
self.cache = LinkedList()
<|end_body_0|>
<|body_start_1|>
if key in self.map:
node = self.map[key]
self.cache.remove(node)
self.cache.append(node)
return node.value
return -1
... | LRUCache | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, value):
""":type key: int :type value: int :rtype: void"""
<|body_2|>
<|end_s... | stack_v2_sparse_classes_10k_train_005242 | 2,543 | permissive | [
{
"docstring": ":type capacity: int",
"name": "__init__",
"signature": "def __init__(self, capacity)"
},
{
"docstring": ":type key: int :rtype: int",
"name": "get",
"signature": "def get(self, key)"
},
{
"docstring": ":type key: int :type value: int :rtype: void",
"name": "pu... | 3 | stack_v2_sparse_classes_30k_train_006706 | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: void | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: void
<|sk... | ba84c192fb9995dd48ddc6d81c3153488dd3c698 | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, value):
""":type key: int :type value: int :rtype: void"""
<|body_2|>
<|end_s... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
self.capacity = capacity
self.map = {}
self.cache = LinkedList()
def get(self, key):
""":type key: int :rtype: int"""
if key in self.map:
node = self.map[key]
self.cac... | the_stack_v2_python_sparse | Python/lru-cache.py | phucle2411/LeetCode | train | 0 | |
9da2e5b13d4d669d3402defae75d2399f16b14f1 | [
"e = 2.718281828459045\nctx.save_for_backward(x, l, u, g)\ny = l + (u - l) / (1 + e ** (-g * x))\nreturn y",
"e = 2.718281828459045\nx, l, u, g = ctx.saved_tensors\ndzdx = dzdy * (g * (u - l) * e ** (-g * x) / (1 + e ** (-g * x)) ** 2)\ndzdl = dzdy * (1 / (e ** (g * x) + 1))\ndzdu = dzdy * (1 / (e ** (-g * x) + 1... | <|body_start_0|>
e = 2.718281828459045
ctx.save_for_backward(x, l, u, g)
y = l + (u - l) / (1 + e ** (-g * x))
return y
<|end_body_0|>
<|body_start_1|>
e = 2.718281828459045
x, l, u, g = ctx.saved_tensors
dzdx = dzdy * (g * (u - l) * e ** (-g * x) / (1 + e ** (-g... | GeneralizedLogistic | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GeneralizedLogistic:
def forward(ctx, x, l, u, g):
"""Computes the generalized logistic function Arguments --------- ctx: A PyTorch context object x: (Tensor) of size (T x n), the input features l, u, and g: (scalar tensors) representing the generalized logistic function parameters. Retu... | stack_v2_sparse_classes_10k_train_005243 | 1,489 | no_license | [
{
"docstring": "Computes the generalized logistic function Arguments --------- ctx: A PyTorch context object x: (Tensor) of size (T x n), the input features l, u, and g: (scalar tensors) representing the generalized logistic function parameters. Returns ------- y: (Tensor) of size (T x n), the outputs of the ge... | 2 | stack_v2_sparse_classes_30k_train_003557 | Implement the Python class `GeneralizedLogistic` described below.
Class description:
Implement the GeneralizedLogistic class.
Method signatures and docstrings:
- def forward(ctx, x, l, u, g): Computes the generalized logistic function Arguments --------- ctx: A PyTorch context object x: (Tensor) of size (T x n), the ... | Implement the Python class `GeneralizedLogistic` described below.
Class description:
Implement the GeneralizedLogistic class.
Method signatures and docstrings:
- def forward(ctx, x, l, u, g): Computes the generalized logistic function Arguments --------- ctx: A PyTorch context object x: (Tensor) of size (T x n), the ... | b6824c340272f65b8c5fd44fcea2a363a7e69f05 | <|skeleton|>
class GeneralizedLogistic:
def forward(ctx, x, l, u, g):
"""Computes the generalized logistic function Arguments --------- ctx: A PyTorch context object x: (Tensor) of size (T x n), the input features l, u, and g: (scalar tensors) representing the generalized logistic function parameters. Retu... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class GeneralizedLogistic:
def forward(ctx, x, l, u, g):
"""Computes the generalized logistic function Arguments --------- ctx: A PyTorch context object x: (Tensor) of size (T x n), the input features l, u, and g: (scalar tensors) representing the generalized logistic function parameters. Returns ------- y:... | the_stack_v2_python_sparse | homework/Kirsten_Ziman_HW1/generalized_logistic.py | KirstensGitHub/deep_learning | train | 0 | |
c427da84ab06c5444ba7a11ffcf50fe6081643b2 | [
"if verbosity:\n (print >> self.stdout, 'Project settings:')\n (print >> self.stdout, 'Configuration definition file placed at %r\\n' % AVAILABLE_SETTINGS.path)\n for setting in AVAILABLE_SETTINGS:\n indent = ' ' * 4\n if is_settings_container(setting):\n (print >> self.stdout, '%s... | <|body_start_0|>
if verbosity:
(print >> self.stdout, 'Project settings:')
(print >> self.stdout, 'Configuration definition file placed at %r\n' % AVAILABLE_SETTINGS.path)
for setting in AVAILABLE_SETTINGS:
indent = ' ' * 4
if is_settings_conta... | Command | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Command:
def check_setman(self, verbosity):
"""Check setman configuration."""
<|body_0|>
def handle_noargs(self, **options):
"""Do all necessary things."""
<|body_1|>
def store_default_values(self, verbosity):
"""Store default values to Settings ... | stack_v2_sparse_classes_10k_train_005244 | 2,852 | permissive | [
{
"docstring": "Check setman configuration.",
"name": "check_setman",
"signature": "def check_setman(self, verbosity)"
},
{
"docstring": "Do all necessary things.",
"name": "handle_noargs",
"signature": "def handle_noargs(self, **options)"
},
{
"docstring": "Store default values ... | 3 | stack_v2_sparse_classes_30k_train_002850 | Implement the Python class `Command` described below.
Class description:
Implement the Command class.
Method signatures and docstrings:
- def check_setman(self, verbosity): Check setman configuration.
- def handle_noargs(self, **options): Do all necessary things.
- def store_default_values(self, verbosity): Store def... | Implement the Python class `Command` described below.
Class description:
Implement the Command class.
Method signatures and docstrings:
- def check_setman(self, verbosity): Check setman configuration.
- def handle_noargs(self, **options): Do all necessary things.
- def store_default_values(self, verbosity): Store def... | 08fc786b0d7ad0216129c62e4907d6aa79643739 | <|skeleton|>
class Command:
def check_setman(self, verbosity):
"""Check setman configuration."""
<|body_0|>
def handle_noargs(self, **options):
"""Do all necessary things."""
<|body_1|>
def store_default_values(self, verbosity):
"""Store default values to Settings ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Command:
def check_setman(self, verbosity):
"""Check setman configuration."""
if verbosity:
(print >> self.stdout, 'Project settings:')
(print >> self.stdout, 'Configuration definition file placed at %r\n' % AVAILABLE_SETTINGS.path)
for setting in AVAILABLE_... | the_stack_v2_python_sparse | setman/frameworks/django_setman/management/commands/setman_cmd.py | playpauseandstop/setman | train | 2 | |
8ff0d16459494647e04ac06b010654e2d9e6a7cd | [
"sanitized_week_day_str = week_day_str.upper()\nif sanitized_week_day_str not in cls.__members__:\n raise AttributeError(f'Invalid Week Day passed: \"{week_day_str}\"')\nreturn cls[sanitized_week_day_str]",
"if isinstance(day, WeekDay):\n return day\nreturn cls.get_weekday_number(week_day_str=day)",
"if n... | <|body_start_0|>
sanitized_week_day_str = week_day_str.upper()
if sanitized_week_day_str not in cls.__members__:
raise AttributeError(f'Invalid Week Day passed: "{week_day_str}"')
return cls[sanitized_week_day_str]
<|end_body_0|>
<|body_start_1|>
if isinstance(day, WeekDay):... | Python Enum containing Days of the Week. | WeekDay | [
"Apache-2.0",
"BSD-3-Clause",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WeekDay:
"""Python Enum containing Days of the Week."""
def get_weekday_number(cls, week_day_str: str):
"""Return the ISO Week Day Number for a Week Day. :param week_day_str: Full Name of the Week Day. Example: "Sunday" :return: ISO Week Day Number corresponding to the provided Weekd... | stack_v2_sparse_classes_10k_train_005245 | 2,675 | permissive | [
{
"docstring": "Return the ISO Week Day Number for a Week Day. :param week_day_str: Full Name of the Week Day. Example: \"Sunday\" :return: ISO Week Day Number corresponding to the provided Weekday",
"name": "get_weekday_number",
"signature": "def get_weekday_number(cls, week_day_str: str)"
},
{
... | 3 | null | Implement the Python class `WeekDay` described below.
Class description:
Python Enum containing Days of the Week.
Method signatures and docstrings:
- def get_weekday_number(cls, week_day_str: str): Return the ISO Week Day Number for a Week Day. :param week_day_str: Full Name of the Week Day. Example: "Sunday" :return... | Implement the Python class `WeekDay` described below.
Class description:
Python Enum containing Days of the Week.
Method signatures and docstrings:
- def get_weekday_number(cls, week_day_str: str): Return the ISO Week Day Number for a Week Day. :param week_day_str: Full Name of the Week Day. Example: "Sunday" :return... | 1b122c15030e99cef9d4ff26d3781a7a9d6949bc | <|skeleton|>
class WeekDay:
"""Python Enum containing Days of the Week."""
def get_weekday_number(cls, week_day_str: str):
"""Return the ISO Week Day Number for a Week Day. :param week_day_str: Full Name of the Week Day. Example: "Sunday" :return: ISO Week Day Number corresponding to the provided Weekd... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class WeekDay:
"""Python Enum containing Days of the Week."""
def get_weekday_number(cls, week_day_str: str):
"""Return the ISO Week Day Number for a Week Day. :param week_day_str: Full Name of the Week Day. Example: "Sunday" :return: ISO Week Day Number corresponding to the provided Weekday"""
... | the_stack_v2_python_sparse | airflow/utils/weekday.py | apache/airflow | train | 22,756 |
00f91c62fd9a5410eb6cb531a9f5c199871001f4 | [
"super(Stance, self).__init__()\nself.src_encoder = encoder\nif tgt_encoder is None:\n self.tgt_encoder = encoder\nelse:\n self.tgt_encoder = tgt_encoder\nself.CNN = CNN(cnn_increasing, cnn_num_layers, cnn_filter_counts)\nself.loss = BCEWithLogitsLoss()",
"pos_score = self.score_pair(query, pos, query_mask,... | <|body_start_0|>
super(Stance, self).__init__()
self.src_encoder = encoder
if tgt_encoder is None:
self.tgt_encoder = encoder
else:
self.tgt_encoder = tgt_encoder
self.CNN = CNN(cnn_increasing, cnn_num_layers, cnn_filter_counts)
self.loss = BCEWith... | "STANCE first gets character embeddings. Next, LSTM runs over char embeddings to get char representations. Then, similarity matrix created where all LSTM embeddings are scored for similarity using dot product. Optimal Transport is then run over the similarity matrix to align weights. Finally, CNN detects features in al... | Stance | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Stance:
""""STANCE first gets character embeddings. Next, LSTM runs over char embeddings to get char representations. Then, similarity matrix created where all LSTM embeddings are scored for similarity using dot product. Optimal Transport is then run over the similarity matrix to align weights. F... | stack_v2_sparse_classes_10k_train_005246 | 8,806 | permissive | [
{
"docstring": "param config: config object param vocab: vocab object param max_len_token: max number of tokens",
"name": "__init__",
"signature": "def __init__(self, encoder, cnn_increasing, cnn_num_layers, cnn_filter_counts, tgt_encoder=None)"
},
{
"docstring": "Compute loss for batch of query... | 3 | stack_v2_sparse_classes_30k_train_006207 | Implement the Python class `Stance` described below.
Class description:
"STANCE first gets character embeddings. Next, LSTM runs over char embeddings to get char representations. Then, similarity matrix created where all LSTM embeddings are scored for similarity using dot product. Optimal Transport is then run over th... | Implement the Python class `Stance` described below.
Class description:
"STANCE first gets character embeddings. Next, LSTM runs over char embeddings to get char representations. Then, similarity matrix created where all LSTM embeddings are scored for similarity using dot product. Optimal Transport is then run over th... | 5dca6fa477c6fdb93b042deb1b0212bb91ce7f00 | <|skeleton|>
class Stance:
""""STANCE first gets character embeddings. Next, LSTM runs over char embeddings to get char representations. Then, similarity matrix created where all LSTM embeddings are scored for similarity using dot product. Optimal Transport is then run over the similarity matrix to align weights. F... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Stance:
""""STANCE first gets character embeddings. Next, LSTM runs over char embeddings to get char representations. Then, similarity matrix created where all LSTM embeddings are scored for similarity using dot product. Optimal Transport is then run over the similarity matrix to align weights. Finally, CNN d... | the_stack_v2_python_sparse | stance.py | jlibovicky/neural-string-edit-distance | train | 2 |
252b5415aeb413e64e87b927c93f63474bf5ce65 | [
"set_seed(int(time()))\ntokenizer = create_tokenizer(tokenizer)\nmodel = create_model(model)\nself._text_generation_pipiline = _create_pipiline(tokenizer, model, device, framework)",
"seqs = [seqs] if isinstance(seqs, str) else seqs\nmax_length = max(map(len, seqs)) * 2\nreturn self._text_generation_pipiline(seqs... | <|body_start_0|>
set_seed(int(time()))
tokenizer = create_tokenizer(tokenizer)
model = create_model(model)
self._text_generation_pipiline = _create_pipiline(tokenizer, model, device, framework)
<|end_body_0|>
<|body_start_1|>
seqs = [seqs] if isinstance(seqs, str) else seqs
... | Text generator pipiline. | TextGenerator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TextGenerator:
"""Text generator pipiline."""
def __init__(self, tokenizer, model, device=-1, framework='pt'):
"""Init class object."""
<|body_0|>
def __call__(self, seqs):
"""Call class object."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
se... | stack_v2_sparse_classes_10k_train_005247 | 1,880 | no_license | [
{
"docstring": "Init class object.",
"name": "__init__",
"signature": "def __init__(self, tokenizer, model, device=-1, framework='pt')"
},
{
"docstring": "Call class object.",
"name": "__call__",
"signature": "def __call__(self, seqs)"
}
] | 2 | stack_v2_sparse_classes_30k_train_005917 | Implement the Python class `TextGenerator` described below.
Class description:
Text generator pipiline.
Method signatures and docstrings:
- def __init__(self, tokenizer, model, device=-1, framework='pt'): Init class object.
- def __call__(self, seqs): Call class object. | Implement the Python class `TextGenerator` described below.
Class description:
Text generator pipiline.
Method signatures and docstrings:
- def __init__(self, tokenizer, model, device=-1, framework='pt'): Init class object.
- def __call__(self, seqs): Call class object.
<|skeleton|>
class TextGenerator:
"""Text ... | b6e52ed56928ea3e67327c46eb021dd3bfd5b4f3 | <|skeleton|>
class TextGenerator:
"""Text generator pipiline."""
def __init__(self, tokenizer, model, device=-1, framework='pt'):
"""Init class object."""
<|body_0|>
def __call__(self, seqs):
"""Call class object."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TextGenerator:
"""Text generator pipiline."""
def __init__(self, tokenizer, model, device=-1, framework='pt'):
"""Init class object."""
set_seed(int(time()))
tokenizer = create_tokenizer(tokenizer)
model = create_model(model)
self._text_generation_pipiline = _creat... | the_stack_v2_python_sparse | gpt/gptrun.py | erdzhemadinov/MADE_FINAL_PROJECT | train | 0 |
cbb07959a07111fd9ce8e3da1b30504cfc95f76a | [
"super().__init__()\nself.forward_func = forward_func\nself.fgsm = FGSM(forward_func, loss_func)\nself.bound = lambda x: torch.clamp(x, min=lower_bound, max=upper_bound)",
"def _clip(inputs: Tensor, outputs: Tensor) -> Tensor:\n diff = outputs - inputs\n if norm == 'Linf':\n return inputs + torch.cla... | <|body_start_0|>
super().__init__()
self.forward_func = forward_func
self.fgsm = FGSM(forward_func, loss_func)
self.bound = lambda x: torch.clamp(x, min=lower_bound, max=upper_bound)
<|end_body_0|>
<|body_start_1|>
def _clip(inputs: Tensor, outputs: Tensor) -> Tensor:
... | Projected Gradient Descent is an iterative version of the one-step attack FGSM that can generate adversarial examples. It takes multiple gradient steps to search for an adversarial perturbation within the desired neighbor ball around the original inputs. In a non-targeted attack, the formulation is:: x_0 = x x_(t+1) = ... | PGD | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PGD:
"""Projected Gradient Descent is an iterative version of the one-step attack FGSM that can generate adversarial examples. It takes multiple gradient steps to search for an adversarial perturbation within the desired neighbor ball around the original inputs. In a non-targeted attack, the form... | stack_v2_sparse_classes_10k_train_005248 | 10,165 | permissive | [
{
"docstring": "Args: forward_func (Callable): The pytorch model for which the attack is computed. loss_func (Callable, optional): Loss function of which the gradient computed. The loss function should take in outputs of the model and labels, and return the loss for each input tensor. The default loss function ... | 3 | stack_v2_sparse_classes_30k_train_005534 | Implement the Python class `PGD` described below.
Class description:
Projected Gradient Descent is an iterative version of the one-step attack FGSM that can generate adversarial examples. It takes multiple gradient steps to search for an adversarial perturbation within the desired neighbor ball around the original inp... | Implement the Python class `PGD` described below.
Class description:
Projected Gradient Descent is an iterative version of the one-step attack FGSM that can generate adversarial examples. It takes multiple gradient steps to search for an adversarial perturbation within the desired neighbor ball around the original inp... | 945c582cc0b08885c4e2bfecb020abdfac0122f3 | <|skeleton|>
class PGD:
"""Projected Gradient Descent is an iterative version of the one-step attack FGSM that can generate adversarial examples. It takes multiple gradient steps to search for an adversarial perturbation within the desired neighbor ball around the original inputs. In a non-targeted attack, the form... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PGD:
"""Projected Gradient Descent is an iterative version of the one-step attack FGSM that can generate adversarial examples. It takes multiple gradient steps to search for an adversarial perturbation within the desired neighbor ball around the original inputs. In a non-targeted attack, the formulation is:: ... | the_stack_v2_python_sparse | captum/robust/_core/pgd.py | pytorch/captum | train | 4,230 |
8413b7d86917ba9e26b2fc6aba161653862e9457 | [
"if not self._errors:\n self._errors = ErrorDict()\nself._errors['upload_of_work'] = self.error_class([DEF_NO_UPLOAD])",
"cleaned_data = self.cleaned_data\nupload = cleaned_data.get('upload_of_work')\nif not upload:\n raise gci_forms.ValidationError(DEF_NO_UPLOAD)\nreturn upload"
] | <|body_start_0|>
if not self._errors:
self._errors = ErrorDict()
self._errors['upload_of_work'] = self.error_class([DEF_NO_UPLOAD])
<|end_body_0|>
<|body_start_1|>
cleaned_data = self.cleaned_data
upload = cleaned_data.get('upload_of_work')
if not upload:
... | Django form for submitting work as file. | WorkSubmissionFileForm | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WorkSubmissionFileForm:
"""Django form for submitting work as file."""
def addFileRequiredError(self):
"""Appends a form error message indicating that this field is required."""
<|body_0|>
def clean_upload_of_work(self):
"""Ensure that file field has data."""
... | stack_v2_sparse_classes_10k_train_005249 | 26,251 | permissive | [
{
"docstring": "Appends a form error message indicating that this field is required.",
"name": "addFileRequiredError",
"signature": "def addFileRequiredError(self)"
},
{
"docstring": "Ensure that file field has data.",
"name": "clean_upload_of_work",
"signature": "def clean_upload_of_wor... | 2 | null | Implement the Python class `WorkSubmissionFileForm` described below.
Class description:
Django form for submitting work as file.
Method signatures and docstrings:
- def addFileRequiredError(self): Appends a form error message indicating that this field is required.
- def clean_upload_of_work(self): Ensure that file f... | Implement the Python class `WorkSubmissionFileForm` described below.
Class description:
Django form for submitting work as file.
Method signatures and docstrings:
- def addFileRequiredError(self): Appends a form error message indicating that this field is required.
- def clean_upload_of_work(self): Ensure that file f... | f581989f168189fa3a58c028eff327a16c03e438 | <|skeleton|>
class WorkSubmissionFileForm:
"""Django form for submitting work as file."""
def addFileRequiredError(self):
"""Appends a form error message indicating that this field is required."""
<|body_0|>
def clean_upload_of_work(self):
"""Ensure that file field has data."""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class WorkSubmissionFileForm:
"""Django form for submitting work as file."""
def addFileRequiredError(self):
"""Appends a form error message indicating that this field is required."""
if not self._errors:
self._errors = ErrorDict()
self._errors['upload_of_work'] = self.error... | the_stack_v2_python_sparse | app/soc/modules/gci/views/task.py | sambitgaan/nupic.son | train | 0 |
7b03886651aed082dbce29f2c2b6121a3af1a264 | [
"ports = []\nfor start, end in self.term.destination_port:\n if start == end:\n ports.append(str(start))\n else:\n ports.append('%d-%d' % (start, end))\nreturn ports",
"settings = [str(x) for x in self.term.logging]\nif any((value in settings for value in ['true', 'True'])):\n return True\n... | <|body_start_0|>
ports = []
for start, end in self.term.destination_port:
if start == end:
ports.append(str(start))
else:
ports.append('%d-%d' % (start, end))
return ports
<|end_body_0|>
<|body_start_1|>
settings = [str(x) for x in... | A Term object. | Term | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Term:
"""A Term object."""
def _GetPorts(self):
"""Return a port or port range in string format."""
<|body_0|>
def _GetLoggingSetting(self):
"""Return true if a term indicates that logging is desired."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_10k_train_005250 | 5,001 | permissive | [
{
"docstring": "Return a port or port range in string format.",
"name": "_GetPorts",
"signature": "def _GetPorts(self)"
},
{
"docstring": "Return true if a term indicates that logging is desired.",
"name": "_GetLoggingSetting",
"signature": "def _GetLoggingSetting(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_007197 | Implement the Python class `Term` described below.
Class description:
A Term object.
Method signatures and docstrings:
- def _GetPorts(self): Return a port or port range in string format.
- def _GetLoggingSetting(self): Return true if a term indicates that logging is desired. | Implement the Python class `Term` described below.
Class description:
A Term object.
Method signatures and docstrings:
- def _GetPorts(self): Return a port or port range in string format.
- def _GetLoggingSetting(self): Return true if a term indicates that logging is desired.
<|skeleton|>
class Term:
"""A Term o... | d145ca447e0e04895507777b8c5834c22e90df11 | <|skeleton|>
class Term:
"""A Term object."""
def _GetPorts(self):
"""Return a port or port range in string format."""
<|body_0|>
def _GetLoggingSetting(self):
"""Return true if a term indicates that logging is desired."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Term:
"""A Term object."""
def _GetPorts(self):
"""Return a port or port range in string format."""
ports = []
for start, end in self.term.destination_port:
if start == end:
ports.append(str(start))
else:
ports.append('%d-%d'... | the_stack_v2_python_sparse | capirca/lib/gcp.py | google/capirca | train | 743 |
699ee7c085ae75873aad30a4a81c344c6e758275 | [
"super().__init__()\nif nn_embedding is not None:\n self.embedding = nn.Embedding.from_pretrained(nn_embedding)\nelif sum(field_sizes) is not None and embed_size is not None:\n self.embedding = nn.Embedding(sum(field_sizes), embed_size, **kwargs)\nelse:\n raise ValueError('missing required arguments')\nsel... | <|body_start_0|>
super().__init__()
if nn_embedding is not None:
self.embedding = nn.Embedding.from_pretrained(nn_embedding)
elif sum(field_sizes) is not None and embed_size is not None:
self.embedding = nn.Embedding(sum(field_sizes), embed_size, **kwargs)
else:
... | Base Input class for embedding indices in multi fields of inputs, which is more efficient than embedding with a number of SingleIndexEmbedding. | MultiIndicesEmbedding | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiIndicesEmbedding:
"""Base Input class for embedding indices in multi fields of inputs, which is more efficient than embedding with a number of SingleIndexEmbedding."""
def __init__(self, embed_size: Optional[int]=None, field_sizes: Optional[List[int]]=None, nn_embedding: Optional[nn.Par... | stack_v2_sparse_classes_10k_train_005251 | 4,009 | permissive | [
{
"docstring": "Initialize MultiIndicesEmbedding. Args: embed_size (int): size of embedding tensor. Defaults to None field_sizes (List[int]): list of inputs fields' sizes. Defaults to None nn_embedding (nn.Parameter, optional): pretrained embedding values. Defaults to None device (str): device of torch. Default... | 4 | stack_v2_sparse_classes_30k_val_000090 | Implement the Python class `MultiIndicesEmbedding` described below.
Class description:
Base Input class for embedding indices in multi fields of inputs, which is more efficient than embedding with a number of SingleIndexEmbedding.
Method signatures and docstrings:
- def __init__(self, embed_size: Optional[int]=None, ... | Implement the Python class `MultiIndicesEmbedding` described below.
Class description:
Base Input class for embedding indices in multi fields of inputs, which is more efficient than embedding with a number of SingleIndexEmbedding.
Method signatures and docstrings:
- def __init__(self, embed_size: Optional[int]=None, ... | 751a43b9cd35e951d81c0d9cf46507b1777bb7ff | <|skeleton|>
class MultiIndicesEmbedding:
"""Base Input class for embedding indices in multi fields of inputs, which is more efficient than embedding with a number of SingleIndexEmbedding."""
def __init__(self, embed_size: Optional[int]=None, field_sizes: Optional[List[int]]=None, nn_embedding: Optional[nn.Par... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MultiIndicesEmbedding:
"""Base Input class for embedding indices in multi fields of inputs, which is more efficient than embedding with a number of SingleIndexEmbedding."""
def __init__(self, embed_size: Optional[int]=None, field_sizes: Optional[List[int]]=None, nn_embedding: Optional[nn.Parameter]=None,... | the_stack_v2_python_sparse | torecsys/inputs/base/multi_indices_emb.py | p768lwy3/torecsys | train | 98 |
775c4e98a11283314c39bc56d3be0b1b42ab3c1d | [
"re = ''\nself.d[self.idx] = longUrl\nn = self.idx\nwhile n:\n re += self.code[n % 62]\n n /= 62\nself.idx += 1\nreturn re",
"i = 0\nfor x in shortUrl:\n if 'a' <= x <= 'z':\n i = i * 62 + ord(x) - ord('a')\n elif 'A' <= x <= 'Z':\n i = i * 62 + ord(x) - ord('A') + 26\n else:\n ... | <|body_start_0|>
re = ''
self.d[self.idx] = longUrl
n = self.idx
while n:
re += self.code[n % 62]
n /= 62
self.idx += 1
return re
<|end_body_0|>
<|body_start_1|>
i = 0
for x in shortUrl:
if 'a' <= x <= 'z':
... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def encode(self, longUrl):
"""Encodes a URL to a shortened URL. :type longUrl: str :rtype: str"""
<|body_0|>
def decode(self, shortUrl):
"""Decodes a shortened URL to its original URL. :type shortUrl: str :rtype: str"""
<|body_1|>
<|end_skeleton|>
<|... | stack_v2_sparse_classes_10k_train_005252 | 1,069 | no_license | [
{
"docstring": "Encodes a URL to a shortened URL. :type longUrl: str :rtype: str",
"name": "encode",
"signature": "def encode(self, longUrl)"
},
{
"docstring": "Decodes a shortened URL to its original URL. :type shortUrl: str :rtype: str",
"name": "decode",
"signature": "def decode(self,... | 2 | null | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def encode(self, longUrl): Encodes a URL to a shortened URL. :type longUrl: str :rtype: str
- def decode(self, shortUrl): Decodes a shortened URL to its original URL. :type shortUrl: s... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def encode(self, longUrl): Encodes a URL to a shortened URL. :type longUrl: str :rtype: str
- def decode(self, shortUrl): Decodes a shortened URL to its original URL. :type shortUrl: s... | 20623defecf65cbc35b194d8b60d8b211816ee4f | <|skeleton|>
class Codec:
def encode(self, longUrl):
"""Encodes a URL to a shortened URL. :type longUrl: str :rtype: str"""
<|body_0|>
def decode(self, shortUrl):
"""Decodes a shortened URL to its original URL. :type shortUrl: str :rtype: str"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Codec:
def encode(self, longUrl):
"""Encodes a URL to a shortened URL. :type longUrl: str :rtype: str"""
re = ''
self.d[self.idx] = longUrl
n = self.idx
while n:
re += self.code[n % 62]
n /= 62
self.idx += 1
return re
def dec... | the_stack_v2_python_sparse | in_Python/0535 Encode and Decode TinyURL.py | YangLiyli131/Leetcode2020 | train | 0 | |
28e494e4b8b335cd133e9b0871810fa45783225b | [
"setting = JsonSetting(settingFilePath)\nself.bd_rest_api = setting.get('bd_rest_api')\nself.oauth = setting.get('oauth')\nself.others = setting.get('others')\nself.access_token = self.get_oauth_token()",
"headers = {'content-type': 'application/json'}\nurl = self.bd_rest_api['domain']\nurl += ':' + self.bd_rest_... | <|body_start_0|>
setting = JsonSetting(settingFilePath)
self.bd_rest_api = setting.get('bd_rest_api')
self.oauth = setting.get('oauth')
self.others = setting.get('others')
self.access_token = self.get_oauth_token()
<|end_body_0|>
<|body_start_1|>
headers = {'content-type... | Building Depot Helpe Class | BuildingDepotHelper | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BuildingDepotHelper:
"""Building Depot Helpe Class"""
def __init__(self, settingFilePath='./knockingSettings.json'):
"""Initialize instance and load settings"""
<|body_0|>
def get_oauth_token(self):
"""Get OAuth access token"""
<|body_1|>
def get_tim... | stack_v2_sparse_classes_10k_train_005253 | 4,153 | permissive | [
{
"docstring": "Initialize instance and load settings",
"name": "__init__",
"signature": "def __init__(self, settingFilePath='./knockingSettings.json')"
},
{
"docstring": "Get OAuth access token",
"name": "get_oauth_token",
"signature": "def get_oauth_token(self)"
},
{
"docstring... | 4 | stack_v2_sparse_classes_30k_train_000781 | Implement the Python class `BuildingDepotHelper` described below.
Class description:
Building Depot Helpe Class
Method signatures and docstrings:
- def __init__(self, settingFilePath='./knockingSettings.json'): Initialize instance and load settings
- def get_oauth_token(self): Get OAuth access token
- def get_timeser... | Implement the Python class `BuildingDepotHelper` described below.
Class description:
Building Depot Helpe Class
Method signatures and docstrings:
- def __init__(self, settingFilePath='./knockingSettings.json'): Initialize instance and load settings
- def get_oauth_token(self): Get OAuth access token
- def get_timeser... | d7e8237f13cb264f9c772b343e2830ebe1319662 | <|skeleton|>
class BuildingDepotHelper:
"""Building Depot Helpe Class"""
def __init__(self, settingFilePath='./knockingSettings.json'):
"""Initialize instance and load settings"""
<|body_0|>
def get_oauth_token(self):
"""Get OAuth access token"""
<|body_1|>
def get_tim... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class BuildingDepotHelper:
"""Building Depot Helpe Class"""
def __init__(self, settingFilePath='./knockingSettings.json'):
"""Initialize instance and load settings"""
setting = JsonSetting(settingFilePath)
self.bd_rest_api = setting.get('bd_rest_api')
self.oauth = setting.get('o... | the_stack_v2_python_sparse | BuildingDepotHelper.py | gs27/Edge-Analytics | train | 0 |
dfe5b57c1f7747701557b2fd3e3d936fbce6c806 | [
"loader = DatasetAnnotationLoader(is_full=self.is_full, data_path=self.data_path, cache_path=self.cache_path, verbose=self.verbose)\nyield {'train': loader.load_trainval_data()}\nyield {'train01': loader.load_train_data()}\nyield {'val01': loader.load_val_data()}\nyield {'test': loader.load_test_data()}",
"args =... | <|body_start_0|>
loader = DatasetAnnotationLoader(is_full=self.is_full, data_path=self.data_path, cache_path=self.cache_path, verbose=self.verbose)
yield {'train': loader.load_trainval_data()}
yield {'train01': loader.load_train_data()}
yield {'val01': loader.load_val_data()}
yie... | MPII Keypoints preprocessing functions. | Keypoints | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Keypoints:
"""MPII Keypoints preprocessing functions."""
def load_data(self):
"""Load data of the dataset (create a generator)."""
<|body_0|>
def process_set_metadata(self, data, set_name):
"""Saves the metadata of a set."""
<|body_1|>
<|end_skeleton|>
... | stack_v2_sparse_classes_10k_train_005254 | 40,392 | permissive | [
{
"docstring": "Load data of the dataset (create a generator).",
"name": "load_data",
"signature": "def load_data(self)"
},
{
"docstring": "Saves the metadata of a set.",
"name": "process_set_metadata",
"signature": "def process_set_metadata(self, data, set_name)"
}
] | 2 | stack_v2_sparse_classes_30k_train_007282 | Implement the Python class `Keypoints` described below.
Class description:
MPII Keypoints preprocessing functions.
Method signatures and docstrings:
- def load_data(self): Load data of the dataset (create a generator).
- def process_set_metadata(self, data, set_name): Saves the metadata of a set. | Implement the Python class `Keypoints` described below.
Class description:
MPII Keypoints preprocessing functions.
Method signatures and docstrings:
- def load_data(self): Load data of the dataset (create a generator).
- def process_set_metadata(self, data, set_name): Saves the metadata of a set.
<|skeleton|>
class ... | e0be95d941b50a5b2e27ffa1c5be20dc6aa2d6a1 | <|skeleton|>
class Keypoints:
"""MPII Keypoints preprocessing functions."""
def load_data(self):
"""Load data of the dataset (create a generator)."""
<|body_0|>
def process_set_metadata(self, data, set_name):
"""Saves the metadata of a set."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Keypoints:
"""MPII Keypoints preprocessing functions."""
def load_data(self):
"""Load data of the dataset (create a generator)."""
loader = DatasetAnnotationLoader(is_full=self.is_full, data_path=self.data_path, cache_path=self.cache_path, verbose=self.verbose)
yield {'train': loa... | the_stack_v2_python_sparse | dbcollection/datasets/mpii_pose/keypoints.py | dbcollection/dbcollection | train | 25 |
5a72f648c91433466e9fcaeca14595ef16a158db | [
"super(FSSD, self).__init__(p, alpha)\nself.k = k\nself.V = V\nself.null_sim = null_sim",
"alpha = self.alpha\nnull_sim = self.null_sim\nn_simulate = null_sim.n_simulate\nn = X.shape[0]\nJ = self.V.shape[0]\nnfssd, fea_tensor = self.statistic(X, return_feature_tensor=True)\nsim_results = null_sim.simulate(self, X... | <|body_start_0|>
super(FSSD, self).__init__(p, alpha)
self.k = k
self.V = V
self.null_sim = null_sim
<|end_body_0|>
<|body_start_1|>
alpha = self.alpha
null_sim = self.null_sim
n_simulate = null_sim.n_simulate
n = X.shape[0]
J = self.V.shape[0]
... | Goodness-of-fit test using The Finite Set Stein Discrepancy statistic. and a set of paired test locations. The statistic is n*FSSD^2. The statistic can be negative because of the unbiased estimator. :math:`H0` : the sample follows :math:`p` :math:`H1` : the sample does not follow :math:`p` :math:`p` is specified to the... | FSSD | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FSSD:
"""Goodness-of-fit test using The Finite Set Stein Discrepancy statistic. and a set of paired test locations. The statistic is n*FSSD^2. The statistic can be negative because of the unbiased estimator. :math:`H0` : the sample follows :math:`p` :math:`H1` : the sample does not follow :math:`... | stack_v2_sparse_classes_10k_train_005255 | 16,140 | permissive | [
{
"docstring": "Parameters ---------- p : an instance of UnnormalizedDensity k : a DifferentiableKernel object V : J x dx numpy array of J locations to test the difference null_sim : an instance of H0Simulator for simulating from the null distribution. alpha : significance level",
"name": "__init__",
"s... | 5 | stack_v2_sparse_classes_30k_train_003999 | Implement the Python class `FSSD` described below.
Class description:
Goodness-of-fit test using The Finite Set Stein Discrepancy statistic. and a set of paired test locations. The statistic is n*FSSD^2. The statistic can be negative because of the unbiased estimator. :math:`H0` : the sample follows :math:`p` :math:`H... | Implement the Python class `FSSD` described below.
Class description:
Goodness-of-fit test using The Finite Set Stein Discrepancy statistic. and a set of paired test locations. The statistic is n*FSSD^2. The statistic can be negative because of the unbiased estimator. :math:`H0` : the sample follows :math:`p` :math:`H... | 9e7fc39f215a7f2b9174ab02bcf71a36067d7e19 | <|skeleton|>
class FSSD:
"""Goodness-of-fit test using The Finite Set Stein Discrepancy statistic. and a set of paired test locations. The statistic is n*FSSD^2. The statistic can be negative because of the unbiased estimator. :math:`H0` : the sample follows :math:`p` :math:`H1` : the sample does not follow :math:`... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class FSSD:
"""Goodness-of-fit test using The Finite Set Stein Discrepancy statistic. and a set of paired test locations. The statistic is n*FSSD^2. The statistic can be negative because of the unbiased estimator. :math:`H0` : the sample follows :math:`p` :math:`H1` : the sample does not follow :math:`p` :math:`p` ... | the_stack_v2_python_sparse | hyppo/kgof/fssd.py | neurodata/hyppo | train | 186 |
2e75f3f70ab13799d3b163d4f2873035a0de5839 | [
"self.active = False\nLabel.__init__(self, name, None, rect, background_color)\nself.return_callback = return_callback\nreturn",
"if len(keydown_event.unicode) and unicodedata.category(keydown_event.unicode)[0] in 'LNPSZ':\n self.text = self.text + keydown_event.unicode\nelif keydown_event.key == pygame.K_BACK... | <|body_start_0|>
self.active = False
Label.__init__(self, name, None, rect, background_color)
self.return_callback = return_callback
return
<|end_body_0|>
<|body_start_1|>
if len(keydown_event.unicode) and unicodedata.category(keydown_event.unicode)[0] in 'LNPSZ':
se... | A box where the user can type text. To actually receive key events, the TextBox must be registered with the Display using Display.key_sensitive(TextBox). Attributes: TextBox.text Standard Label attribute, holding the text typed so far. TextBox.active Boolean flag whether this TextBox is active, initally False. | TextBox | [
"Unlicense"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TextBox:
"""A box where the user can type text. To actually receive key events, the TextBox must be registered with the Display using Display.key_sensitive(TextBox). Attributes: TextBox.text Standard Label attribute, holding the text typed so far. TextBox.active Boolean flag whether this TextBox ... | stack_v2_sparse_classes_10k_train_005256 | 27,668 | permissive | [
{
"docstring": "Initialise the TextBox. If return_callback is given, return_callback(TextBox.text) will be called when [RETURN] is pressed.",
"name": "__init__",
"signature": "def __init__(self, name, rect, return_callback=None, background_color=(250, 250, 250))"
},
{
"docstring": "If printable,... | 5 | stack_v2_sparse_classes_30k_val_000329 | Implement the Python class `TextBox` described below.
Class description:
A box where the user can type text. To actually receive key events, the TextBox must be registered with the Display using Display.key_sensitive(TextBox). Attributes: TextBox.text Standard Label attribute, holding the text typed so far. TextBox.ac... | Implement the Python class `TextBox` described below.
Class description:
A box where the user can type text. To actually receive key events, the TextBox must be registered with the Display using Display.key_sensitive(TextBox). Attributes: TextBox.text Standard Label attribute, holding the text typed so far. TextBox.ac... | c2fc3d4e9beedb8487cfa4bfa13bdf55ec36af97 | <|skeleton|>
class TextBox:
"""A box where the user can type text. To actually receive key events, the TextBox must be registered with the Display using Display.key_sensitive(TextBox). Attributes: TextBox.text Standard Label attribute, holding the text typed so far. TextBox.active Boolean flag whether this TextBox ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TextBox:
"""A box where the user can type text. To actually receive key events, the TextBox must be registered with the Display using Display.key_sensitive(TextBox). Attributes: TextBox.text Standard Label attribute, holding the text typed so far. TextBox.active Boolean flag whether this TextBox is active, in... | the_stack_v2_python_sparse | reference_scripts/clickndrag-0.4.1/clickndrag/gui.py | stivosaurus/rpi-snippets | train | 1 |
e50d4668751b33b8d5505a300d5236347070edc0 | [
"if not isinstance(typecode, ElementDeclaration):\n return False\ntry:\n nsuri, ncname = typecode.substitutionGroup\nexcept (AttributeError, TypeError):\n return False\nif (nsuri, ncname) != (self.schema, self.literal):\n if not nsuri and (not self.schema) and (ncname == self.literal):\n return T... | <|body_start_0|>
if not isinstance(typecode, ElementDeclaration):
return False
try:
nsuri, ncname = typecode.substitutionGroup
except (AttributeError, TypeError):
return False
if (nsuri, ncname) != (self.schema, self.literal):
if not nsuri ... | Typecodes subclass to represent a Global Element Declaration by setting class variables schema and literal. schema = namespaceURI literal = NCName substitutionGroup -- GED reference of form, (namespaceURI,NCName) | ElementDeclaration | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ElementDeclaration:
"""Typecodes subclass to represent a Global Element Declaration by setting class variables schema and literal. schema = namespaceURI literal = NCName substitutionGroup -- GED reference of form, (namespaceURI,NCName)"""
def checkSubstitute(self, typecode):
"""If th... | stack_v2_sparse_classes_10k_train_005257 | 14,557 | permissive | [
{
"docstring": "If this is True, allow typecode to be substituted for \"self\" typecode.",
"name": "checkSubstitute",
"signature": "def checkSubstitute(self, typecode)"
},
{
"docstring": "if elt matches a member of the head substitutionGroup, return the GED typecode representation of the member.... | 2 | stack_v2_sparse_classes_30k_train_006124 | Implement the Python class `ElementDeclaration` described below.
Class description:
Typecodes subclass to represent a Global Element Declaration by setting class variables schema and literal. schema = namespaceURI literal = NCName substitutionGroup -- GED reference of form, (namespaceURI,NCName)
Method signatures and... | Implement the Python class `ElementDeclaration` described below.
Class description:
Typecodes subclass to represent a Global Element Declaration by setting class variables schema and literal. schema = namespaceURI literal = NCName substitutionGroup -- GED reference of form, (namespaceURI,NCName)
Method signatures and... | 9b890e6a25471037b7485e4999b480de7c86b656 | <|skeleton|>
class ElementDeclaration:
"""Typecodes subclass to represent a Global Element Declaration by setting class variables schema and literal. schema = namespaceURI literal = NCName substitutionGroup -- GED reference of form, (namespaceURI,NCName)"""
def checkSubstitute(self, typecode):
"""If th... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ElementDeclaration:
"""Typecodes subclass to represent a Global Element Declaration by setting class variables schema and literal. schema = namespaceURI literal = NCName substitutionGroup -- GED reference of form, (namespaceURI,NCName)"""
def checkSubstitute(self, typecode):
"""If this is True, a... | the_stack_v2_python_sparse | Libraries/DUTs/Community/di_vsphere/pysphere/pysphere/ZSI/schema.py | Spirent/iTest-assets | train | 10 |
6de0436abd47ba94fac9bb05fdbe77550bf7c91f | [
"super().__init__(*args, **kargs)\nself.set_field_from_dict('token')\nself.fields['token'].help_text = _('Authentication token provided by the external platform.')",
"form_data = super().clean()\nself.store_field_in_dict('token')\nreturn form_data"
] | <|body_start_0|>
super().__init__(*args, **kargs)
self.set_field_from_dict('token')
self.fields['token'].help_text = _('Authentication token provided by the external platform.')
<|end_body_0|>
<|body_start_1|>
form_data = super().clean()
self.store_field_in_dict('token')
... | Form to include a token field. | JSONTokenForm | [
"MIT",
"LGPL-2.0-or-later",
"Python-2.0",
"BSD-3-Clause",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class JSONTokenForm:
"""Form to include a token field."""
def __init__(self, *args, **kargs):
"""Modify the fields with the adequate information."""
<|body_0|>
def clean(self):
"""Verify form values."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
sup... | stack_v2_sparse_classes_10k_train_005258 | 20,237 | permissive | [
{
"docstring": "Modify the fields with the adequate information.",
"name": "__init__",
"signature": "def __init__(self, *args, **kargs)"
},
{
"docstring": "Verify form values.",
"name": "clean",
"signature": "def clean(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_005316 | Implement the Python class `JSONTokenForm` described below.
Class description:
Form to include a token field.
Method signatures and docstrings:
- def __init__(self, *args, **kargs): Modify the fields with the adequate information.
- def clean(self): Verify form values. | Implement the Python class `JSONTokenForm` described below.
Class description:
Form to include a token field.
Method signatures and docstrings:
- def __init__(self, *args, **kargs): Modify the fields with the adequate information.
- def clean(self): Verify form values.
<|skeleton|>
class JSONTokenForm:
"""Form t... | 5473e9faa24c71a2a1102d47ebc2cbf27608e42a | <|skeleton|>
class JSONTokenForm:
"""Form to include a token field."""
def __init__(self, *args, **kargs):
"""Modify the fields with the adequate information."""
<|body_0|>
def clean(self):
"""Verify form values."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class JSONTokenForm:
"""Form to include a token field."""
def __init__(self, *args, **kargs):
"""Modify the fields with the adequate information."""
super().__init__(*args, **kargs)
self.set_field_from_dict('token')
self.fields['token'].help_text = _('Authentication token provid... | the_stack_v2_python_sparse | ontask/action/forms/run.py | LucasFranciscoCorreia/ontask_b | train | 0 |
c820c3c7e7dcfe12689941e64853d6cfd77af07a | [
"RadianceMaterial.__init__(self, name, materialType='glow', modifier='void')\nself.red = red\n'A positive value for the Red channel of the glow'\nself.green = green\n'A positive value for the Green channel of the glow'\nself.blue = blue\n'A positive value for the Blue channel of the glow'\nself.maxRadius = maxRadiu... | <|body_start_0|>
RadianceMaterial.__init__(self, name, materialType='glow', modifier='void')
self.red = red
'A positive value for the Red channel of the glow'
self.green = green
'A positive value for the Green channel of the glow'
self.blue = blue
'A positive valu... | Create glow material. Attributes: name: Material name as a string. The name should not have whitespaces or special characters. red: A positive value for the Red channel of the glow green: A positive value for the Green channel of the glow blue: A positive value for the Blue channel of the glow maxRadius: ---. | GlowMaterial | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GlowMaterial:
"""Create glow material. Attributes: name: Material name as a string. The name should not have whitespaces or special characters. red: A positive value for the Red channel of the glow green: A positive value for the Green channel of the glow blue: A positive value for the Blue chann... | stack_v2_sparse_classes_10k_train_005259 | 2,004 | permissive | [
{
"docstring": "Init Glow material.",
"name": "__init__",
"signature": "def __init__(self, name, red=0, green=0, blue=0, maxRadius=0)"
},
{
"docstring": "Return full Radiance definition",
"name": "toRadString",
"signature": "def toRadString(self, minimal=False)"
}
] | 2 | stack_v2_sparse_classes_30k_train_000632 | Implement the Python class `GlowMaterial` described below.
Class description:
Create glow material. Attributes: name: Material name as a string. The name should not have whitespaces or special characters. red: A positive value for the Red channel of the glow green: A positive value for the Green channel of the glow bl... | Implement the Python class `GlowMaterial` described below.
Class description:
Create glow material. Attributes: name: Material name as a string. The name should not have whitespaces or special characters. red: A positive value for the Red channel of the glow green: A positive value for the Green channel of the glow bl... | 983fccc934e5546082557f6c2d1f2d9e00eba332 | <|skeleton|>
class GlowMaterial:
"""Create glow material. Attributes: name: Material name as a string. The name should not have whitespaces or special characters. red: A positive value for the Red channel of the glow green: A positive value for the Green channel of the glow blue: A positive value for the Blue chann... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class GlowMaterial:
"""Create glow material. Attributes: name: Material name as a string. The name should not have whitespaces or special characters. red: A positive value for the Red channel of the glow green: A positive value for the Green channel of the glow blue: A positive value for the Blue channel of the glo... | the_stack_v2_python_sparse | honeybee/radiance/material/glow.py | ladybug-tools/honeybee-server | train | 7 |
852d86266232703c304e5b8618d514285eb59aad | [
"assert scope_type in VALID_FILTER_SCOPES, 'Invalid scope type.'\nself.sample_id_set = set(sample_ids)\nself.scope_type = scope_type",
"if self.scope_type == FILTER_SCOPE__ALL:\n intersection = samples_passing_for_variant & self.sample_id_set\n return intersection == self.sample_id_set\nelif self.scope_type... | <|body_start_0|>
assert scope_type in VALID_FILTER_SCOPES, 'Invalid scope type.'
self.sample_id_set = set(sample_ids)
self.scope_type = scope_type
<|end_body_0|>
<|body_start_1|>
if self.scope_type == FILTER_SCOPE__ALL:
intersection = samples_passing_for_variant & self.sampl... | Represents the scope that a filter should be applied over. | FilterScope | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FilterScope:
"""Represents the scope that a filter should be applied over."""
def __init__(self, scope_type, sample_ids):
"""Args: sample_ids: Set of sample ids. scope_type: A scope in VALID_FILTER_SCOPES."""
<|body_0|>
def do_passing_samples_satisfy_scope(self, samples_... | stack_v2_sparse_classes_10k_train_005260 | 2,031 | permissive | [
{
"docstring": "Args: sample_ids: Set of sample ids. scope_type: A scope in VALID_FILTER_SCOPES.",
"name": "__init__",
"signature": "def __init__(self, scope_type, sample_ids)"
},
{
"docstring": "Returns a Boolean indicating whether the samples satisfy the scope.",
"name": "do_passing_sample... | 3 | stack_v2_sparse_classes_30k_train_001781 | Implement the Python class `FilterScope` described below.
Class description:
Represents the scope that a filter should be applied over.
Method signatures and docstrings:
- def __init__(self, scope_type, sample_ids): Args: sample_ids: Set of sample ids. scope_type: A scope in VALID_FILTER_SCOPES.
- def do_passing_samp... | Implement the Python class `FilterScope` described below.
Class description:
Represents the scope that a filter should be applied over.
Method signatures and docstrings:
- def __init__(self, scope_type, sample_ids): Args: sample_ids: Set of sample ids. scope_type: A scope in VALID_FILTER_SCOPES.
- def do_passing_samp... | 898936072a716a799462c113286056690a7723d1 | <|skeleton|>
class FilterScope:
"""Represents the scope that a filter should be applied over."""
def __init__(self, scope_type, sample_ids):
"""Args: sample_ids: Set of sample ids. scope_type: A scope in VALID_FILTER_SCOPES."""
<|body_0|>
def do_passing_samples_satisfy_scope(self, samples_... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class FilterScope:
"""Represents the scope that a filter should be applied over."""
def __init__(self, scope_type, sample_ids):
"""Args: sample_ids: Set of sample ids. scope_type: A scope in VALID_FILTER_SCOPES."""
assert scope_type in VALID_FILTER_SCOPES, 'Invalid scope type.'
self.sam... | the_stack_v2_python_sparse | genome_designer/variants/filter_scope.py | RubensZimbres/millstone | train | 1 |
ebd0adda3d25ec5a178f55e622ebaa639de6de45 | [
"if image_meta is None:\n image_meta = {}\nimage_meta = copy.deepcopy(image_meta)\nimage_meta['properties'] = objects.ImageMetaProps.from_dict(image_meta.get('properties', {}))\nfor fld in NULLABLE_STRING_FIELDS:\n if fld in image_meta and image_meta[fld] is None:\n image_meta[fld] = ''\nfor fld in NUL... | <|body_start_0|>
if image_meta is None:
image_meta = {}
image_meta = copy.deepcopy(image_meta)
image_meta['properties'] = objects.ImageMetaProps.from_dict(image_meta.get('properties', {}))
for fld in NULLABLE_STRING_FIELDS:
if fld in image_meta and image_meta[fld]... | ImageMeta | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ImageMeta:
def from_dict(cls, image_meta):
"""Create instance from image metadata dict :param image_meta: image metadata dictionary Creates a new object instance, initializing from the properties associated with the image metadata instance :returns: an ImageMeta instance"""
<|bod... | stack_v2_sparse_classes_10k_train_005261 | 31,126 | permissive | [
{
"docstring": "Create instance from image metadata dict :param image_meta: image metadata dictionary Creates a new object instance, initializing from the properties associated with the image metadata instance :returns: an ImageMeta instance",
"name": "from_dict",
"signature": "def from_dict(cls, image_... | 3 | stack_v2_sparse_classes_30k_train_004661 | Implement the Python class `ImageMeta` described below.
Class description:
Implement the ImageMeta class.
Method signatures and docstrings:
- def from_dict(cls, image_meta): Create instance from image metadata dict :param image_meta: image metadata dictionary Creates a new object instance, initializing from the prope... | Implement the Python class `ImageMeta` described below.
Class description:
Implement the ImageMeta class.
Method signatures and docstrings:
- def from_dict(cls, image_meta): Create instance from image metadata dict :param image_meta: image metadata dictionary Creates a new object instance, initializing from the prope... | 065c5906d2da3e2bb6eeb3a7a15d4cd8d98b35e9 | <|skeleton|>
class ImageMeta:
def from_dict(cls, image_meta):
"""Create instance from image metadata dict :param image_meta: image metadata dictionary Creates a new object instance, initializing from the properties associated with the image metadata instance :returns: an ImageMeta instance"""
<|bod... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ImageMeta:
def from_dict(cls, image_meta):
"""Create instance from image metadata dict :param image_meta: image metadata dictionary Creates a new object instance, initializing from the properties associated with the image metadata instance :returns: an ImageMeta instance"""
if image_meta is No... | the_stack_v2_python_sparse | nova/objects/image_meta.py | openstack/nova | train | 2,287 | |
8af38bc258b027642cf1db7d75621e7c25c195eb | [
"self.model = KNeighborsClassifier(n_neighbors=3)\nself.X = X\nself.Y = Y",
"if params is None:\n params = [{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20], 'weights': ['uniform', 'distance']}]\nself.CV = KFoldCrossVal(self.X, self.Y, folds=kfold)\nself.CV.tune_and_evaluate(self.model, parameters=param... | <|body_start_0|>
self.model = KNeighborsClassifier(n_neighbors=3)
self.X = X
self.Y = Y
<|end_body_0|>
<|body_start_1|>
if params is None:
params = [{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20], 'weights': ['uniform', 'distance']}]
self.CV = KFoldCrossVal(... | K-nearest neighbor classifier | KNN | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KNN:
"""K-nearest neighbor classifier"""
def __init__(self, X, Y):
""":param X: :param Y:"""
<|body_0|>
def tune_and_eval(self, results_file, params=None, njobs=50, kfold=10):
""":param results_file: :param params: :param njobs: :param kfold: :return:"""
... | stack_v2_sparse_classes_10k_train_005262 | 10,404 | permissive | [
{
"docstring": ":param X: :param Y:",
"name": "__init__",
"signature": "def __init__(self, X, Y)"
},
{
"docstring": ":param results_file: :param params: :param njobs: :param kfold: :return:",
"name": "tune_and_eval",
"signature": "def tune_and_eval(self, results_file, params=None, njobs=... | 3 | stack_v2_sparse_classes_30k_train_003275 | Implement the Python class `KNN` described below.
Class description:
K-nearest neighbor classifier
Method signatures and docstrings:
- def __init__(self, X, Y): :param X: :param Y:
- def tune_and_eval(self, results_file, params=None, njobs=50, kfold=10): :param results_file: :param params: :param njobs: :param kfold:... | Implement the Python class `KNN` described below.
Class description:
K-nearest neighbor classifier
Method signatures and docstrings:
- def __init__(self, X, Y): :param X: :param Y:
- def tune_and_eval(self, results_file, params=None, njobs=50, kfold=10): :param results_file: :param params: :param njobs: :param kfold:... | 127177deb630ad66520a2fdae1793417cd77ee99 | <|skeleton|>
class KNN:
"""K-nearest neighbor classifier"""
def __init__(self, X, Y):
""":param X: :param Y:"""
<|body_0|>
def tune_and_eval(self, results_file, params=None, njobs=50, kfold=10):
""":param results_file: :param params: :param njobs: :param kfold: :return:"""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class KNN:
"""K-nearest neighbor classifier"""
def __init__(self, X, Y):
""":param X: :param Y:"""
self.model = KNeighborsClassifier(n_neighbors=3)
self.X = X
self.Y = Y
def tune_and_eval(self, results_file, params=None, njobs=50, kfold=10):
""":param results_file: ... | the_stack_v2_python_sparse | classifier/classical_classifiers.py | seedpcseed/DiTaxa | train | 0 |
72a5672a6bae08e14ece7c8a608b058e163012e6 | [
"startTime = datetime.datetime.now()\nclient = dml.pymongo.MongoClient()\nrepo = client.repo\nrepo.authenticate('cici_fyl', 'cici_fyl')\npropertydata = repo['cici_fyl.property'].find()\nrestaurantdata = repo['cici_fyl.restaurant'].find()\ncoor = methods.selectcoordinate(restaurantdata)\nx = methods.appendattribute(... | <|body_start_0|>
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('cici_fyl', 'cici_fyl')
propertydata = repo['cici_fyl.property'].find()
restaurantdata = repo['cici_fyl.restaurant'].find()
coor = methods.... | processrestaurant | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class processrestaurant:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document describing everyt... | stack_v2_sparse_classes_10k_train_005263 | 3,335 | permissive | [
{
"docstring": "Retrieve some data sets (not using the API here for the sake of simplicity).",
"name": "execute",
"signature": "def execute(trial=False)"
},
{
"docstring": "Create the provenance document describing everything happening in this script. Each run of the script will generate a new d... | 2 | null | Implement the Python class `processrestaurant` described below.
Class description:
Implement the processrestaurant class.
Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(), startTime... | Implement the Python class `processrestaurant` described below.
Class description:
Implement the processrestaurant class.
Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(), startTime... | 0df485d0469c5451ebdcd684bed2a0960ba3ab84 | <|skeleton|>
class processrestaurant:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document describing everyt... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class processrestaurant:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('cici_fyl', 'cici_fyl')
pr... | the_stack_v2_python_sparse | cici_fyl/project/cici_fyl/processrestaurant.py | lingyigu/course-2017-spr-proj | train | 0 | |
90d9ba94b2779fe3901790cb22932ed1e80e98c9 | [
"if x < 0:\n raise Exception('不能输入负数')\nif x == 0:\n return 0\ncur = -5\nwhile True:\n pre = cur\n cur = (cur + x / cur) / 2\n if abs(cur - pre) < 1e-06:\n return cur",
"if x < 0:\n raise Exception('不能输入负数')\nif x == 0:\n return 0\ncur = 1\nwhile True:\n pre = cur\n cur = (cur + ... | <|body_start_0|>
if x < 0:
raise Exception('不能输入负数')
if x == 0:
return 0
cur = -5
while True:
pre = cur
cur = (cur + x / cur) / 2
if abs(cur - pre) < 1e-06:
return cur
<|end_body_0|>
<|body_start_1|>
if ... | Solution | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def mySqrt(self, x):
""":type x: int :rtype: int"""
<|body_0|>
def mySqrt1(self, x):
""":type x: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if x < 0:
raise Exception('不能输入负数')
if x == 0:
... | stack_v2_sparse_classes_10k_train_005264 | 1,203 | permissive | [
{
"docstring": ":type x: int :rtype: int",
"name": "mySqrt",
"signature": "def mySqrt(self, x)"
},
{
"docstring": ":type x: int :rtype: int",
"name": "mySqrt1",
"signature": "def mySqrt1(self, x)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def mySqrt(self, x): :type x: int :rtype: int
- def mySqrt1(self, x): :type x: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def mySqrt(self, x): :type x: int :rtype: int
- def mySqrt1(self, x): :type x: int :rtype: int
<|skeleton|>
class Solution:
def mySqrt(self, x):
""":type x: int :rt... | b484ae4c4e9f9186232e31f2de11720aebb42968 | <|skeleton|>
class Solution:
def mySqrt(self, x):
""":type x: int :rtype: int"""
<|body_0|>
def mySqrt1(self, x):
""":type x: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def mySqrt(self, x):
""":type x: int :rtype: int"""
if x < 0:
raise Exception('不能输入负数')
if x == 0:
return 0
cur = -5
while True:
pre = cur
cur = (cur + x / cur) / 2
if abs(cur - pre) < 1e-06:
... | the_stack_v2_python_sparse | 17-二分查找/0069_1.py | Sytx74/LeetCode-Solution-Python | train | 0 | |
da693becd45b726478b01c075df233acff71672f | [
"self.current_dir = os.getcwd()\nnow = datetime.now().strftime('%I%p_%m_%d_%Y')\ntest_name = self.__class__.__name__\nself.tempdir = '{}_{}'.format(test_name, now)\nif not os.path.isdir(self.tempdir):\n os.mkdir(self.tempdir)\nos.chdir(self.tempdir)\nopen('test.pdb', 'w').write(test_pdb_str)",
"params_phil = i... | <|body_start_0|>
self.current_dir = os.getcwd()
now = datetime.now().strftime('%I%p_%m_%d_%Y')
test_name = self.__class__.__name__
self.tempdir = '{}_{}'.format(test_name, now)
if not os.path.isdir(self.tempdir):
os.mkdir(self.tempdir)
os.chdir(self.tempdir)
... | TestPDBinterpretationNCSSearch | [
"BSD-3-Clause-LBNL",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestPDBinterpretationNCSSearch:
def setUp(self):
"""Create temporary folder for temp files produced during test"""
<|body_0|>
def test_calling_pdb_interpretation(self):
"""Make sure can create NCS object and change search parameters"""
<|body_1|>
def tea... | stack_v2_sparse_classes_10k_train_005265 | 6,330 | permissive | [
{
"docstring": "Create temporary folder for temp files produced during test",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Make sure can create NCS object and change search parameters",
"name": "test_calling_pdb_interpretation",
"signature": "def test_calling_pdb_in... | 3 | null | Implement the Python class `TestPDBinterpretationNCSSearch` described below.
Class description:
Implement the TestPDBinterpretationNCSSearch class.
Method signatures and docstrings:
- def setUp(self): Create temporary folder for temp files produced during test
- def test_calling_pdb_interpretation(self): Make sure ca... | Implement the Python class `TestPDBinterpretationNCSSearch` described below.
Class description:
Implement the TestPDBinterpretationNCSSearch class.
Method signatures and docstrings:
- def setUp(self): Create temporary folder for temp files produced during test
- def test_calling_pdb_interpretation(self): Make sure ca... | 77d66c719b5746f37af51ad593e2941ed6fbba17 | <|skeleton|>
class TestPDBinterpretationNCSSearch:
def setUp(self):
"""Create temporary folder for temp files produced during test"""
<|body_0|>
def test_calling_pdb_interpretation(self):
"""Make sure can create NCS object and change search parameters"""
<|body_1|>
def tea... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TestPDBinterpretationNCSSearch:
def setUp(self):
"""Create temporary folder for temp files produced during test"""
self.current_dir = os.getcwd()
now = datetime.now().strftime('%I%p_%m_%d_%Y')
test_name = self.__class__.__name__
self.tempdir = '{}_{}'.format(test_name, ... | the_stack_v2_python_sparse | modules/cctbx_project/mmtbx/monomer_library/tst_pdb_interpretation_ncs_processing.py | jorgediazjr/dials-dev20191018 | train | 0 | |
4e795d4488a814ebc494485f02c8f499cea11005 | [
"try:\n blog = Blog.find(year, month, day, slug)\nexcept Blog.DoesNotExist:\n abort(404, message='No such blog')\nexcept ValueError:\n abort(409, message='Multiple blogs found')\nreturn blog",
"try:\n blog = Blog.find(year, month, day, slug)\nexcept Blog.DoesNotExist:\n abort(404, message='No such ... | <|body_start_0|>
try:
blog = Blog.find(year, month, day, slug)
except Blog.DoesNotExist:
abort(404, message='No such blog')
except ValueError:
abort(409, message='Multiple blogs found')
return blog
<|end_body_0|>
<|body_start_1|>
try:
... | BlogAPI | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BlogAPI:
def get(self, year, month, day, slug):
"""Get blog post details"""
<|body_0|>
def patch(self, args, year, month, day, slug):
"""Edit blog post details"""
<|body_1|>
def delete(self, year, month, day, slug):
"""Delete blog post"""
... | stack_v2_sparse_classes_10k_train_005266 | 4,891 | permissive | [
{
"docstring": "Get blog post details",
"name": "get",
"signature": "def get(self, year, month, day, slug)"
},
{
"docstring": "Edit blog post details",
"name": "patch",
"signature": "def patch(self, args, year, month, day, slug)"
},
{
"docstring": "Delete blog post",
"name": ... | 3 | stack_v2_sparse_classes_30k_train_002099 | Implement the Python class `BlogAPI` described below.
Class description:
Implement the BlogAPI class.
Method signatures and docstrings:
- def get(self, year, month, day, slug): Get blog post details
- def patch(self, args, year, month, day, slug): Edit blog post details
- def delete(self, year, month, day, slug): Del... | Implement the Python class `BlogAPI` described below.
Class description:
Implement the BlogAPI class.
Method signatures and docstrings:
- def get(self, year, month, day, slug): Get blog post details
- def patch(self, args, year, month, day, slug): Edit blog post details
- def delete(self, year, month, day, slug): Del... | dffc3b1e16c24dd49e516e36aaa731a8dd299e66 | <|skeleton|>
class BlogAPI:
def get(self, year, month, day, slug):
"""Get blog post details"""
<|body_0|>
def patch(self, args, year, month, day, slug):
"""Edit blog post details"""
<|body_1|>
def delete(self, year, month, day, slug):
"""Delete blog post"""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class BlogAPI:
def get(self, year, month, day, slug):
"""Get blog post details"""
try:
blog = Blog.find(year, month, day, slug)
except Blog.DoesNotExist:
abort(404, message='No such blog')
except ValueError:
abort(409, message='Multiple blogs found... | the_stack_v2_python_sparse | tilda/api/blog.py | tilda-center/backend | train | 0 | |
eed1f56088fe29b3dd51b95f2696fb74890ddf4d | [
"invalid = self.get_invalid(instance)\nif invalid:\n raise RuntimeError('Nodes found with non-unique asset IDs: {0}'.format(invalid))",
"others = [i for i in list(instance.context) if i is not instance and set(cls.families) & get_families(i)]\nif not others:\n return []\nother_ids = defaultdict(list)\nfor o... | <|body_start_0|>
invalid = self.get_invalid(instance)
if invalid:
raise RuntimeError('Nodes found with non-unique asset IDs: {0}'.format(invalid))
<|end_body_0|>
<|body_start_1|>
others = [i for i in list(instance.context) if i is not instance and set(cls.families) & get_families(i)... | Validate nodes across model instances have a unique Colorbleed Id This validates whether the node ids to be published are unique across all model instances currently being published (even if those other instances are DISABLED currently for publishing). This will *NOT* validate against previous publishes or publishes be... | ValidateNodeIdsUniqueInstanceClash | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ValidateNodeIdsUniqueInstanceClash:
"""Validate nodes across model instances have a unique Colorbleed Id This validates whether the node ids to be published are unique across all model instances currently being published (even if those other instances are DISABLED currently for publishing). This ... | stack_v2_sparse_classes_10k_train_005267 | 2,944 | no_license | [
{
"docstring": "Process all meshes",
"name": "process",
"signature": "def process(self, instance)"
},
{
"docstring": "Return the member nodes that are invalid",
"name": "get_invalid",
"signature": "def get_invalid(cls, instance)"
}
] | 2 | stack_v2_sparse_classes_30k_train_001908 | Implement the Python class `ValidateNodeIdsUniqueInstanceClash` described below.
Class description:
Validate nodes across model instances have a unique Colorbleed Id This validates whether the node ids to be published are unique across all model instances currently being published (even if those other instances are DI... | Implement the Python class `ValidateNodeIdsUniqueInstanceClash` described below.
Class description:
Validate nodes across model instances have a unique Colorbleed Id This validates whether the node ids to be published are unique across all model instances currently being published (even if those other instances are DI... | fa1a22297c1b2cfd48c88372958360fe4004524b | <|skeleton|>
class ValidateNodeIdsUniqueInstanceClash:
"""Validate nodes across model instances have a unique Colorbleed Id This validates whether the node ids to be published are unique across all model instances currently being published (even if those other instances are DISABLED currently for publishing). This ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ValidateNodeIdsUniqueInstanceClash:
"""Validate nodes across model instances have a unique Colorbleed Id This validates whether the node ids to be published are unique across all model instances currently being published (even if those other instances are DISABLED currently for publishing). This will *NOT* va... | the_stack_v2_python_sparse | colorbleed/plugins/maya/publish/validate_node_ids_unique_in_asset.py | BigRoy/colorbleed-config | train | 3 |
545d15c4ae69c6b5ce1bdec93aadf562840fdac5 | [
"if node_type == None:\n node_type = node_base\nif edge_type == None:\n edge_type = edge_base\nsuper().__init__()\nself.node_type = node_type\nself.edge_type = edge_type\nself.node_list = []\nself.edge_list = []\nself.mst = None",
"self.unionset = unionset(len(self.node_list), int)\nself.edge_list.sort()\ns... | <|body_start_0|>
if node_type == None:
node_type = node_base
if edge_type == None:
edge_type = edge_base
super().__init__()
self.node_type = node_type
self.edge_type = edge_type
self.node_list = []
self.edge_list = []
self.mst = Non... | mst_base | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class mst_base:
def __init__(self, node_type=None, edge_type=None):
"""vector_list: list, with node value edge_list: list, with edge value edge_type: str, 'list' for adjacent list, 'matrix' for adjacent matrix"""
<|body_0|>
def generate_mst(self):
"""The base function for ... | stack_v2_sparse_classes_10k_train_005268 | 1,213 | no_license | [
{
"docstring": "vector_list: list, with node value edge_list: list, with edge value edge_type: str, 'list' for adjacent list, 'matrix' for adjacent matrix",
"name": "__init__",
"signature": "def __init__(self, node_type=None, edge_type=None)"
},
{
"docstring": "The base function for mst Using Kr... | 2 | stack_v2_sparse_classes_30k_train_002046 | Implement the Python class `mst_base` described below.
Class description:
Implement the mst_base class.
Method signatures and docstrings:
- def __init__(self, node_type=None, edge_type=None): vector_list: list, with node value edge_list: list, with edge value edge_type: str, 'list' for adjacent list, 'matrix' for adj... | Implement the Python class `mst_base` described below.
Class description:
Implement the mst_base class.
Method signatures and docstrings:
- def __init__(self, node_type=None, edge_type=None): vector_list: list, with node value edge_list: list, with edge value edge_type: str, 'list' for adjacent list, 'matrix' for adj... | 2bd6aaadb8f6abcc13e9c468adff74c93b0ae6b2 | <|skeleton|>
class mst_base:
def __init__(self, node_type=None, edge_type=None):
"""vector_list: list, with node value edge_list: list, with edge value edge_type: str, 'list' for adjacent list, 'matrix' for adjacent matrix"""
<|body_0|>
def generate_mst(self):
"""The base function for ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class mst_base:
def __init__(self, node_type=None, edge_type=None):
"""vector_list: list, with node value edge_list: list, with edge value edge_type: str, 'list' for adjacent list, 'matrix' for adjacent matrix"""
if node_type == None:
node_type = node_base
if edge_type == None:
... | the_stack_v2_python_sparse | graph_utils/mstpy/mst_base.py | jrahim/graph_clean | train | 0 | |
1211e5b131221213cb19ce2d4b47a69eb29ed613 | [
"super().__init__(infile, outfile)\nself.infile2 = infile2\nself._default_method = 'fastqutils'",
"self.install_tool('fastqutils')\nif self.infile2 is not None:\n cmd = 'fastqutils tobam -1 {} -2 {} -o {}'.format(self.infile, self.infile2, self.outfile)\nelse:\n cmd = 'fastqutils tobam -1 {} -o {}'.format(s... | <|body_start_0|>
super().__init__(infile, outfile)
self.infile2 = infile2
self._default_method = 'fastqutils'
<|end_body_0|>
<|body_start_1|>
self.install_tool('fastqutils')
if self.infile2 is not None:
cmd = 'fastqutils tobam -1 {} -2 {} -o {}'.format(self.infile, s... | Convert :term:`FASTQ` to :term:`BAM` | FASTQ2BAM | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FASTQ2BAM:
"""Convert :term:`FASTQ` to :term:`BAM`"""
def __init__(self, infile, outfile, infile2=None, *args, **kwargs):
""":param str infile: The path to the input FASTA file. :param str outfile: The path to the output file."""
<|body_0|>
def _method_fastqutils(self, *... | stack_v2_sparse_classes_10k_train_005269 | 1,716 | permissive | [
{
"docstring": ":param str infile: The path to the input FASTA file. :param str outfile: The path to the output file.",
"name": "__init__",
"signature": "def __init__(self, infile, outfile, infile2=None, *args, **kwargs)"
},
{
"docstring": "Converts a fastq file to an unaligned bam file",
"n... | 2 | stack_v2_sparse_classes_30k_train_004769 | Implement the Python class `FASTQ2BAM` described below.
Class description:
Convert :term:`FASTQ` to :term:`BAM`
Method signatures and docstrings:
- def __init__(self, infile, outfile, infile2=None, *args, **kwargs): :param str infile: The path to the input FASTA file. :param str outfile: The path to the output file.
... | Implement the Python class `FASTQ2BAM` described below.
Class description:
Convert :term:`FASTQ` to :term:`BAM`
Method signatures and docstrings:
- def __init__(self, infile, outfile, infile2=None, *args, **kwargs): :param str infile: The path to the input FASTA file. :param str outfile: The path to the output file.
... | 60a746290e763fd1041732dab0bda123841e5b26 | <|skeleton|>
class FASTQ2BAM:
"""Convert :term:`FASTQ` to :term:`BAM`"""
def __init__(self, infile, outfile, infile2=None, *args, **kwargs):
""":param str infile: The path to the input FASTA file. :param str outfile: The path to the output file."""
<|body_0|>
def _method_fastqutils(self, *... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class FASTQ2BAM:
"""Convert :term:`FASTQ` to :term:`BAM`"""
def __init__(self, infile, outfile, infile2=None, *args, **kwargs):
""":param str infile: The path to the input FASTA file. :param str outfile: The path to the output file."""
super().__init__(infile, outfile)
self.infile2 = in... | the_stack_v2_python_sparse | bioconvert/fastq2bam.py | ddesvillechabrol/bioconvert | train | 1 |
0d60b7b8526aa669ba65b13104a262556c82576a | [
"if keys is None:\n keys = ['ymin', 'xmin', 'ymax', 'xmax']\nelif len(keys) != 4:\n raise ValueError('BoundingBox expects 4 keys but got {}'.format(len(keys)))\nself._prefix = prefix\nself._keys = keys\nself._full_keys = [prefix + k for k in keys]\nsuper(BoundingBox, self).__init__(self._full_keys)",
"sides... | <|body_start_0|>
if keys is None:
keys = ['ymin', 'xmin', 'ymax', 'xmax']
elif len(keys) != 4:
raise ValueError('BoundingBox expects 4 keys but got {}'.format(len(keys)))
self._prefix = prefix
self._keys = keys
self._full_keys = [prefix + k for k in keys]
... | An ItemHandler that concatenates a set of parsed Tensors to Bounding Boxes. | BoundingBox | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BoundingBox:
"""An ItemHandler that concatenates a set of parsed Tensors to Bounding Boxes."""
def __init__(self, keys=None, prefix=None):
"""Initialize the bounding box handler. Args: keys: A list of four key names representing the ymin, xmin, ymax, mmax prefix: An optional prefix f... | stack_v2_sparse_classes_10k_train_005270 | 15,383 | permissive | [
{
"docstring": "Initialize the bounding box handler. Args: keys: A list of four key names representing the ymin, xmin, ymax, mmax prefix: An optional prefix for each of the bounding box keys. If provided, `prefix` is appended to each key in `keys`. Raises: ValueError: if keys is not `None` and also not a list o... | 2 | null | Implement the Python class `BoundingBox` described below.
Class description:
An ItemHandler that concatenates a set of parsed Tensors to Bounding Boxes.
Method signatures and docstrings:
- def __init__(self, keys=None, prefix=None): Initialize the bounding box handler. Args: keys: A list of four key names representin... | Implement the Python class `BoundingBox` described below.
Class description:
An ItemHandler that concatenates a set of parsed Tensors to Bounding Boxes.
Method signatures and docstrings:
- def __init__(self, keys=None, prefix=None): Initialize the bounding box handler. Args: keys: A list of four key names representin... | cabf6e4f1970dc14302f87414f170de19944bac2 | <|skeleton|>
class BoundingBox:
"""An ItemHandler that concatenates a set of parsed Tensors to Bounding Boxes."""
def __init__(self, keys=None, prefix=None):
"""Initialize the bounding box handler. Args: keys: A list of four key names representing the ymin, xmin, ymax, mmax prefix: An optional prefix f... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class BoundingBox:
"""An ItemHandler that concatenates a set of parsed Tensors to Bounding Boxes."""
def __init__(self, keys=None, prefix=None):
"""Initialize the bounding box handler. Args: keys: A list of four key names representing the ymin, xmin, ymax, mmax prefix: An optional prefix for each of th... | the_stack_v2_python_sparse | Tensorflow_OpenCV_Nightly/source/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py | ryfeus/lambda-packs | train | 1,283 |
cb3734155c0c730f3d201e966eed33c3a665a7a9 | [
"pu = ground_level_m\nlatitude, longitude, _alt = pm.enu2geodetic(pe_m, pn_m, pu, lat0_rad, lon0_rad, ground_level_m, ell=None, deg=False)\necef_x, ecef_y, ecef_z = pm.enu2ecef(pe_m, pn_m, pu, lat0_rad, lon0_rad, ground_level_m, ell=None, deg=False)\nglobal_rotation = Rotation.from_quat(Dubins2DConverter.quaternion... | <|body_start_0|>
pu = ground_level_m
latitude, longitude, _alt = pm.enu2geodetic(pe_m, pn_m, pu, lat0_rad, lon0_rad, ground_level_m, ell=None, deg=False)
ecef_x, ecef_y, ecef_z = pm.enu2ecef(pe_m, pn_m, pu, lat0_rad, lon0_rad, ground_level_m, ell=None, deg=False)
global_rotation = Rotati... | Dubins2DConverter | [
"GPL-3.0-only",
"BSD-3-Clause",
"GPL-1.0-or-later"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Dubins2DConverter:
def convert_data(cls, pn_m, pe_m, alt_m, psi_rad, lat0_rad, lon0_rad, ground_level_m) -> list:
"""This method takes in a dictionary of "raw" 2D Dubins log data, as read from the LogReader class, and returns a populated Episode object."""
<|body_0|>
def qua... | stack_v2_sparse_classes_10k_train_005271 | 2,905 | permissive | [
{
"docstring": "This method takes in a dictionary of \"raw\" 2D Dubins log data, as read from the LogReader class, and returns a populated Episode object.",
"name": "convert_data",
"signature": "def convert_data(cls, pn_m, pe_m, alt_m, psi_rad, lat0_rad, lon0_rad, ground_level_m) -> list"
},
{
"... | 2 | stack_v2_sparse_classes_30k_val_000400 | Implement the Python class `Dubins2DConverter` described below.
Class description:
Implement the Dubins2DConverter class.
Method signatures and docstrings:
- def convert_data(cls, pn_m, pe_m, alt_m, psi_rad, lat0_rad, lon0_rad, ground_level_m) -> list: This method takes in a dictionary of "raw" 2D Dubins log data, as... | Implement the Python class `Dubins2DConverter` described below.
Class description:
Implement the Dubins2DConverter class.
Method signatures and docstrings:
- def convert_data(cls, pn_m, pe_m, alt_m, psi_rad, lat0_rad, lon0_rad, ground_level_m) -> list: This method takes in a dictionary of "raw" 2D Dubins log data, as... | c90a7346f3a2a651adda5b6ead47d4989af59dcc | <|skeleton|>
class Dubins2DConverter:
def convert_data(cls, pn_m, pe_m, alt_m, psi_rad, lat0_rad, lon0_rad, ground_level_m) -> list:
"""This method takes in a dictionary of "raw" 2D Dubins log data, as read from the LogReader class, and returns a populated Episode object."""
<|body_0|>
def qua... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Dubins2DConverter:
def convert_data(cls, pn_m, pe_m, alt_m, psi_rad, lat0_rad, lon0_rad, ground_level_m) -> list:
"""This method takes in a dictionary of "raw" 2D Dubins log data, as read from the LogReader class, and returns a populated Episode object."""
pu = ground_level_m
latitude,... | the_stack_v2_python_sparse | csaf_f16/fgconverter.py | GaloisInc/csaf | train | 11 | |
3844f1743d91d1dde5f0db73e19da8caea6a9f89 | [
"self._delta_t_minutes = delta_time_minutes\nself._t_fusion_mode = t_fusion_mode\nself._transition_matrices = transition_matrices\nself._all_transitions = build_flat_list_of_visits(transition_matrices)\nself._root_node = None",
"for transition in self._all_transitions:\n baby_node = SpatialTimeModelNode.build_... | <|body_start_0|>
self._delta_t_minutes = delta_time_minutes
self._t_fusion_mode = t_fusion_mode
self._transition_matrices = transition_matrices
self._all_transitions = build_flat_list_of_visits(transition_matrices)
self._root_node = None
<|end_body_0|>
<|body_start_1|>
f... | SpatialTimeModelBuilder | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SpatialTimeModelBuilder:
def __init__(self, transition_matrices: List[TransitionMatrix], delta_time_minutes, t_fusion_mode):
"""Basic constructor :param transition_matrices: The transition matrices representing all of the mobility data windows. :param delta_time_minutes: The time interva... | stack_v2_sparse_classes_10k_train_005272 | 5,379 | permissive | [
{
"docstring": "Basic constructor :param transition_matrices: The transition matrices representing all of the mobility data windows. :param delta_time_minutes: The time interval used for considering similarities of nodes during. :param t_fusion_mode: The time fusion mode to employ when consolidating different d... | 6 | stack_v2_sparse_classes_30k_train_007076 | Implement the Python class `SpatialTimeModelBuilder` described below.
Class description:
Implement the SpatialTimeModelBuilder class.
Method signatures and docstrings:
- def __init__(self, transition_matrices: List[TransitionMatrix], delta_time_minutes, t_fusion_mode): Basic constructor :param transition_matrices: Th... | Implement the Python class `SpatialTimeModelBuilder` described below.
Class description:
Implement the SpatialTimeModelBuilder class.
Method signatures and docstrings:
- def __init__(self, transition_matrices: List[TransitionMatrix], delta_time_minutes, t_fusion_mode): Basic constructor :param transition_matrices: Th... | b058185ca028abd1902edbb35a52d3565b06f8b0 | <|skeleton|>
class SpatialTimeModelBuilder:
def __init__(self, transition_matrices: List[TransitionMatrix], delta_time_minutes, t_fusion_mode):
"""Basic constructor :param transition_matrices: The transition matrices representing all of the mobility data windows. :param delta_time_minutes: The time interva... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SpatialTimeModelBuilder:
def __init__(self, transition_matrices: List[TransitionMatrix], delta_time_minutes, t_fusion_mode):
"""Basic constructor :param transition_matrices: The transition matrices representing all of the mobility data windows. :param delta_time_minutes: The time interval used for con... | the_stack_v2_python_sparse | stm/SpatialTimeModelBuilder.py | s0lver/stm-creator | train | 0 | |
38603ef08b999a2ea644b28054d4b631ceac36f1 | [
"rndstate = randstate(seed)\nrnd = lambda x, *y: rndstate.rand(*y) * (x[1] - x[0]) + x[0]\nnbindings = rndstate.choice(self.nbindings)\nsize = rnd(self.size)\nbins = self.bins[1] * scale\nmaxv = min(self.bins[0] - bias, int(size / bins))\npos = np.empty(0, 'f4')\nwhile len(pos) != nbindings:\n pos = np.unique(rn... | <|body_start_0|>
rndstate = randstate(seed)
rnd = lambda x, *y: rndstate.rand(*y) * (x[1] - x[0]) + x[0]
nbindings = rndstate.choice(self.nbindings)
size = rnd(self.size)
bins = self.bins[1] * scale
maxv = min(self.bins[0] - bias, int(size / bins))
pos = np.empty(... | Create random experiments & images | ExperimentCreator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ExperimentCreator:
"""Create random experiments & images"""
def experiment(self, seed=None, bias=10, scale=2):
"""create an experiment"""
<|body_0|>
def createimage(self, info, bead):
"""transform bead data into an image"""
<|body_1|>
def createtruth... | stack_v2_sparse_classes_10k_train_005273 | 26,924 | no_license | [
{
"docstring": "create an experiment",
"name": "experiment",
"signature": "def experiment(self, seed=None, bias=10, scale=2)"
},
{
"docstring": "transform bead data into an image",
"name": "createimage",
"signature": "def createimage(self, info, bead)"
},
{
"docstring": "transfor... | 4 | stack_v2_sparse_classes_30k_train_001278 | Implement the Python class `ExperimentCreator` described below.
Class description:
Create random experiments & images
Method signatures and docstrings:
- def experiment(self, seed=None, bias=10, scale=2): create an experiment
- def createimage(self, info, bead): transform bead data into an image
- def createtruth(sel... | Implement the Python class `ExperimentCreator` described below.
Class description:
Create random experiments & images
Method signatures and docstrings:
- def experiment(self, seed=None, bias=10, scale=2): create an experiment
- def createimage(self, info, bead): transform bead data into an image
- def createtruth(sel... | f9534e4fff9775ff45d08d401de61015d4a69e76 | <|skeleton|>
class ExperimentCreator:
"""Create random experiments & images"""
def experiment(self, seed=None, bias=10, scale=2):
"""create an experiment"""
<|body_0|>
def createimage(self, info, bead):
"""transform bead data into an image"""
<|body_1|>
def createtruth... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ExperimentCreator:
"""Create random experiments & images"""
def experiment(self, seed=None, bias=10, scale=2):
"""create an experiment"""
rndstate = randstate(seed)
rnd = lambda x, *y: rndstate.rand(*y) * (x[1] - x[0]) + x[0]
nbindings = rndstate.choice(self.nbindings)
... | the_stack_v2_python_sparse | src/simulator/bindings.py | depixusgenome/trackanalysis | train | 0 |
f72b942970e8d27d1939c7d400d412bad7831328 | [
"for vals in vals_list:\n if vals.get('origin', False) and vals['origin'][0] == ':':\n vals.update({'origin': vals['origin'][1:]})\n if vals.get('origin', False) and vals['origin'][-1] == ':':\n vals.update({'origin': vals['origin'][:-1]})\n return super(StockPicking, self).create(vals)",
"... | <|body_start_0|>
for vals in vals_list:
if vals.get('origin', False) and vals['origin'][0] == ':':
vals.update({'origin': vals['origin'][1:]})
if vals.get('origin', False) and vals['origin'][-1] == ':':
vals.update({'origin': vals['origin'][:-1]})
... | Stock Picking. | StockPicking | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StockPicking:
"""Stock Picking."""
def create(self, vals_list):
"""Overridden create method."""
<|body_0|>
def write(self, vals):
"""Overridden write method."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
for vals in vals_list:
if v... | stack_v2_sparse_classes_10k_train_005274 | 49,476 | no_license | [
{
"docstring": "Overridden create method.",
"name": "create",
"signature": "def create(self, vals_list)"
},
{
"docstring": "Overridden write method.",
"name": "write",
"signature": "def write(self, vals)"
}
] | 2 | stack_v2_sparse_classes_30k_train_002913 | Implement the Python class `StockPicking` described below.
Class description:
Stock Picking.
Method signatures and docstrings:
- def create(self, vals_list): Overridden create method.
- def write(self, vals): Overridden write method. | Implement the Python class `StockPicking` described below.
Class description:
Stock Picking.
Method signatures and docstrings:
- def create(self, vals_list): Overridden create method.
- def write(self, vals): Overridden write method.
<|skeleton|>
class StockPicking:
"""Stock Picking."""
def create(self, val... | 7618a365ac78c0f59390a3a6b5fcdd9f76a5ddec | <|skeleton|>
class StockPicking:
"""Stock Picking."""
def create(self, vals_list):
"""Overridden create method."""
<|body_0|>
def write(self, vals):
"""Overridden write method."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class StockPicking:
"""Stock Picking."""
def create(self, vals_list):
"""Overridden create method."""
for vals in vals_list:
if vals.get('origin', False) and vals['origin'][0] == ':':
vals.update({'origin': vals['origin'][1:]})
if vals.get('origin', False... | the_stack_v2_python_sparse | fleet_operations/models/fleet_service.py | JayVora-SerpentCS/fleet_management | train | 29 |
d73af10547f6b8d92a5debc38b9ffd2694363908 | [
"news = response.xpath(\"//a[@target='_blank']\")\nfor new in news:\n item = CDagency()\n item['col_name'] = 'CD06dangxiao'\n href = new.xpath('./@href').extract_first()\n detail_url = 'https://www.cddx.gov.cn' + href\n item['detail_url'] = detail_url\n yield scrapy.Request(detail_url, callback=se... | <|body_start_0|>
news = response.xpath("//a[@target='_blank']")
for new in news:
item = CDagency()
item['col_name'] = 'CD06dangxiao'
href = new.xpath('./@href').extract_first()
detail_url = 'https://www.cddx.gov.cn' + href
item['detail_url'] = ... | CdDangxiaoSpider | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CdDangxiaoSpider:
def parse(self, response):
""""默认的解析回调函数"""
<|body_0|>
def get_text(self, response):
"""获取详细的文本信息"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
news = response.xpath("//a[@target='_blank']")
for new in news:
i... | stack_v2_sparse_classes_10k_train_005275 | 2,954 | no_license | [
{
"docstring": "\"默认的解析回调函数",
"name": "parse",
"signature": "def parse(self, response)"
},
{
"docstring": "获取详细的文本信息",
"name": "get_text",
"signature": "def get_text(self, response)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003771 | Implement the Python class `CdDangxiaoSpider` described below.
Class description:
Implement the CdDangxiaoSpider class.
Method signatures and docstrings:
- def parse(self, response): "默认的解析回调函数
- def get_text(self, response): 获取详细的文本信息 | Implement the Python class `CdDangxiaoSpider` described below.
Class description:
Implement the CdDangxiaoSpider class.
Method signatures and docstrings:
- def parse(self, response): "默认的解析回调函数
- def get_text(self, response): 获取详细的文本信息
<|skeleton|>
class CdDangxiaoSpider:
def parse(self, response):
""""... | d2d66206d799afbfe68cafcc9bd7cd6d9533685d | <|skeleton|>
class CdDangxiaoSpider:
def parse(self, response):
""""默认的解析回调函数"""
<|body_0|>
def get_text(self, response):
"""获取详细的文本信息"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class CdDangxiaoSpider:
def parse(self, response):
""""默认的解析回调函数"""
news = response.xpath("//a[@target='_blank']")
for new in news:
item = CDagency()
item['col_name'] = 'CD06dangxiao'
href = new.xpath('./@href').extract_first()
detail_url = 'ht... | the_stack_v2_python_sparse | CDagency/spiders/cd06_dangxiao.py | gongdx/CDagency | train | 0 | |
81f0d921f6bda0dcc0fd4617ba98e08ac85130a3 | [
"super(BasicAligner, self).__init__()\nself._extensions = [palign().extension]\nself._outext = palign().extension\nself._name = 'basic'",
"if isinstance(input_wav, float) is True:\n duration = input_wav\nelse:\n try:\n wav_speech = sppas.src.audiodata.aio.open(input_wav)\n duration = wav_speec... | <|body_start_0|>
super(BasicAligner, self).__init__()
self._extensions = [palign().extension]
self._outext = palign().extension
self._name = 'basic'
<|end_body_0|>
<|body_start_1|>
if isinstance(input_wav, float) is True:
duration = input_wav
else:
... | Basic automatic alignment system. :author: Brigitte Bigi :organization: Laboratoire Parole et Langage, Aix-en-Provence, France :contact: develop@sppas.org :license: GPL, v3 :copyright: Copyright (C) 2011-2018 Brigitte Bigi This segmentation assign the same duration to each phoneme. In case of phonetic variants, the fir... | BasicAligner | [
"GFDL-1.1-or-later",
"GPL-3.0-only",
"GPL-3.0-or-later",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BasicAligner:
"""Basic automatic alignment system. :author: Brigitte Bigi :organization: Laboratoire Parole et Langage, Aix-en-Provence, France :contact: develop@sppas.org :license: GPL, v3 :copyright: Copyright (C) 2011-2018 Brigitte Bigi This segmentation assign the same duration to each phonem... | stack_v2_sparse_classes_10k_train_005276 | 6,949 | permissive | [
{
"docstring": "Create a BasicAligner instance. This class allows to align one unit assigning the same duration to each phoneme. It selects the shortest sequence in case of variants. :param model_dir: (str) Ignored.",
"name": "__init__",
"signature": "def __init__(self, model_dir=None)"
},
{
"do... | 5 | null | Implement the Python class `BasicAligner` described below.
Class description:
Basic automatic alignment system. :author: Brigitte Bigi :organization: Laboratoire Parole et Langage, Aix-en-Provence, France :contact: develop@sppas.org :license: GPL, v3 :copyright: Copyright (C) 2011-2018 Brigitte Bigi This segmentation ... | Implement the Python class `BasicAligner` described below.
Class description:
Basic automatic alignment system. :author: Brigitte Bigi :organization: Laboratoire Parole et Langage, Aix-en-Provence, France :contact: develop@sppas.org :license: GPL, v3 :copyright: Copyright (C) 2011-2018 Brigitte Bigi This segmentation ... | 3167b65f576abcc27a8767d24c274a04712bd948 | <|skeleton|>
class BasicAligner:
"""Basic automatic alignment system. :author: Brigitte Bigi :organization: Laboratoire Parole et Langage, Aix-en-Provence, France :contact: develop@sppas.org :license: GPL, v3 :copyright: Copyright (C) 2011-2018 Brigitte Bigi This segmentation assign the same duration to each phonem... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class BasicAligner:
"""Basic automatic alignment system. :author: Brigitte Bigi :organization: Laboratoire Parole et Langage, Aix-en-Provence, France :contact: develop@sppas.org :license: GPL, v3 :copyright: Copyright (C) 2011-2018 Brigitte Bigi This segmentation assign the same duration to each phoneme. In case of... | the_stack_v2_python_sparse | sppas/sppas/src/annotations/Align/aligners/basicalign.py | mirfan899/MTTS | train | 0 |
019c9362a9d03118b14561e470d5de8aafeae4aa | [
"threading.Thread.__init__(self)\nself.direction = 'neutral'\nself.id = identity\nself.position = position\nself.move_joint(self.position, 900)",
"while self.direction != '':\n if self.direction == 'decrease':\n if self.position > 200:\n self.position -= 15\n elif self.direction == 'increa... | <|body_start_0|>
threading.Thread.__init__(self)
self.direction = 'neutral'
self.id = identity
self.position = position
self.move_joint(self.position, 900)
<|end_body_0|>
<|body_start_1|>
while self.direction != '':
if self.direction == 'decrease':
... | A Joint class representing a moving node on parts of the emubot | Joint | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Joint:
"""A Joint class representing a moving node on parts of the emubot"""
def __init__(self, identity, position):
"""__init__(identity, position): parameters represent identification and position"""
<|body_0|>
def run(self):
"""Step through a sequence of moves... | stack_v2_sparse_classes_10k_train_005277 | 2,748 | permissive | [
{
"docstring": "__init__(identity, position): parameters represent identification and position",
"name": "__init__",
"signature": "def __init__(self, identity, position)"
},
{
"docstring": "Step through a sequence of moves",
"name": "run",
"signature": "def run(self)"
},
{
"docst... | 3 | stack_v2_sparse_classes_30k_train_000772 | Implement the Python class `Joint` described below.
Class description:
A Joint class representing a moving node on parts of the emubot
Method signatures and docstrings:
- def __init__(self, identity, position): __init__(identity, position): parameters represent identification and position
- def run(self): Step throug... | Implement the Python class `Joint` described below.
Class description:
A Joint class representing a moving node on parts of the emubot
Method signatures and docstrings:
- def __init__(self, identity, position): __init__(identity, position): parameters represent identification and position
- def run(self): Step throug... | a39dc01f7c1213c8079216d49d376b317efbf5f3 | <|skeleton|>
class Joint:
"""A Joint class representing a moving node on parts of the emubot"""
def __init__(self, identity, position):
"""__init__(identity, position): parameters represent identification and position"""
<|body_0|>
def run(self):
"""Step through a sequence of moves... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Joint:
"""A Joint class representing a moving node on parts of the emubot"""
def __init__(self, identity, position):
"""__init__(identity, position): parameters represent identification and position"""
threading.Thread.__init__(self)
self.direction = 'neutral'
self.id = id... | the_stack_v2_python_sparse | Client-Code-2018/CurrentEmuBotCode2/basic_classes.py | maxgodfrey2004/RoboCup-2018-Driving-Code | train | 1 |
44635dea5130342b4c472cd00307d82ed1808b76 | [
"if isinstance(value, dict):\n value = BytesIO(bytes(value.values()))\nmultipolygon = value\nif multipolygon is not None:\n try:\n zip_file = zipfile.ZipFile(value.temporary_file_path())\n except AttributeError:\n zip_file = zipfile.ZipFile(value)\n try:\n shpfile = get_shapefile(zi... | <|body_start_0|>
if isinstance(value, dict):
value = BytesIO(bytes(value.values()))
multipolygon = value
if multipolygon is not None:
try:
zip_file = zipfile.ZipFile(value.temporary_file_path())
except AttributeError:
zip_file =... | Custom Field for Shapefile | ShapeFileField | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ShapeFileField:
"""Custom Field for Shapefile"""
def to_internal_value(self, value):
"""Custom Conversion for shapefile field"""
<|body_0|>
def to_representation(self, value):
"""Custom conversion to representation for ShapeFileField"""
<|body_1|>
<|end_... | stack_v2_sparse_classes_10k_train_005278 | 6,527 | permissive | [
{
"docstring": "Custom Conversion for shapefile field",
"name": "to_internal_value",
"signature": "def to_internal_value(self, value)"
},
{
"docstring": "Custom conversion to representation for ShapeFileField",
"name": "to_representation",
"signature": "def to_representation(self, value)... | 2 | stack_v2_sparse_classes_30k_train_006106 | Implement the Python class `ShapeFileField` described below.
Class description:
Custom Field for Shapefile
Method signatures and docstrings:
- def to_internal_value(self, value): Custom Conversion for shapefile field
- def to_representation(self, value): Custom conversion to representation for ShapeFileField | Implement the Python class `ShapeFileField` described below.
Class description:
Custom Field for Shapefile
Method signatures and docstrings:
- def to_internal_value(self, value): Custom Conversion for shapefile field
- def to_representation(self, value): Custom conversion to representation for ShapeFileField
<|skele... | 5faff50a2f3575f0df91a6b20afe37d43a592381 | <|skeleton|>
class ShapeFileField:
"""Custom Field for Shapefile"""
def to_internal_value(self, value):
"""Custom Conversion for shapefile field"""
<|body_0|>
def to_representation(self, value):
"""Custom conversion to representation for ShapeFileField"""
<|body_1|>
<|end_... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ShapeFileField:
"""Custom Field for Shapefile"""
def to_internal_value(self, value):
"""Custom Conversion for shapefile field"""
if isinstance(value, dict):
value = BytesIO(bytes(value.values()))
multipolygon = value
if multipolygon is not None:
try... | the_stack_v2_python_sparse | tasking/serializers/location.py | onaio/tasking | train | 6 |
44ce3adabec2f72deb48105623e1b45820cd7580 | [
"self.VirtuosoObj = Virtuoso(netlist, wave_names)\nself.netlist = self.VirtuosoObj.netlist\nself.waves = self.VirtuosoObj.waves\npass",
"self.assertIsInstance(self.netlist, str)\nself.assertIsInstance(self.waves, dict)\npass"
] | <|body_start_0|>
self.VirtuosoObj = Virtuoso(netlist, wave_names)
self.netlist = self.VirtuosoObj.netlist
self.waves = self.VirtuosoObj.waves
pass
<|end_body_0|>
<|body_start_1|>
self.assertIsInstance(self.netlist, str)
self.assertIsInstance(self.waves, dict)
pas... | TestVirtuosoTypes | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestVirtuosoTypes:
def setUp(self):
"""Setup function TestTypes for class Virtuoso"""
<|body_0|>
def test_types(self):
"""Function to test data types for class Virtuoso"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.VirtuosoObj = Virtuoso(netl... | stack_v2_sparse_classes_10k_train_005279 | 973 | permissive | [
{
"docstring": "Setup function TestTypes for class Virtuoso",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Function to test data types for class Virtuoso",
"name": "test_types",
"signature": "def test_types(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006055 | Implement the Python class `TestVirtuosoTypes` described below.
Class description:
Implement the TestVirtuosoTypes class.
Method signatures and docstrings:
- def setUp(self): Setup function TestTypes for class Virtuoso
- def test_types(self): Function to test data types for class Virtuoso | Implement the Python class `TestVirtuosoTypes` described below.
Class description:
Implement the TestVirtuosoTypes class.
Method signatures and docstrings:
- def setUp(self): Setup function TestTypes for class Virtuoso
- def test_types(self): Function to test data types for class Virtuoso
<|skeleton|>
class TestVirt... | 825a0eab64be709efe161b9a48eb54c4bc5c1bef | <|skeleton|>
class TestVirtuosoTypes:
def setUp(self):
"""Setup function TestTypes for class Virtuoso"""
<|body_0|>
def test_types(self):
"""Function to test data types for class Virtuoso"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TestVirtuosoTypes:
def setUp(self):
"""Setup function TestTypes for class Virtuoso"""
self.VirtuosoObj = Virtuoso(netlist, wave_names)
self.netlist = self.VirtuosoObj.netlist
self.waves = self.VirtuosoObj.waves
pass
def test_types(self):
"""Function to test... | the_stack_v2_python_sparse | VLC_devel/class_structure/__auto_gen__/test_Virtuoso.py | wenh81/vlc_simulator | train | 0 | |
f8847d1c5a362efb57abaa7c0831d17b4d569a38 | [
"self._reauth_entry = None\nself._email = None\nself._region = None",
"errors = {}\nif user_input is not None:\n self._email = user_input[CONF_EMAIL]\n self._region = user_input[CONF_REGION]\n unique_id = user_input[CONF_EMAIL].lower()\n await self.async_set_unique_id(unique_id)\n if not self._reau... | <|body_start_0|>
self._reauth_entry = None
self._email = None
self._region = None
<|end_body_0|>
<|body_start_1|>
errors = {}
if user_input is not None:
self._email = user_input[CONF_EMAIL]
self._region = user_input[CONF_REGION]
unique_id = us... | Handle a config flow for Mazda Connected Services. | MazdaConfigFlow | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MazdaConfigFlow:
"""Handle a config flow for Mazda Connected Services."""
def __init__(self):
"""Start the mazda config flow."""
<|body_0|>
async def async_step_user(self, user_input=None):
"""Handle the initial step."""
<|body_1|>
async def async_st... | stack_v2_sparse_classes_10k_train_005280 | 3,926 | permissive | [
{
"docstring": "Start the mazda config flow.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Handle the initial step.",
"name": "async_step_user",
"signature": "async def async_step_user(self, user_input=None)"
},
{
"docstring": "Perform reauth if the u... | 3 | null | Implement the Python class `MazdaConfigFlow` described below.
Class description:
Handle a config flow for Mazda Connected Services.
Method signatures and docstrings:
- def __init__(self): Start the mazda config flow.
- async def async_step_user(self, user_input=None): Handle the initial step.
- async def async_step_r... | Implement the Python class `MazdaConfigFlow` described below.
Class description:
Handle a config flow for Mazda Connected Services.
Method signatures and docstrings:
- def __init__(self): Start the mazda config flow.
- async def async_step_user(self, user_input=None): Handle the initial step.
- async def async_step_r... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class MazdaConfigFlow:
"""Handle a config flow for Mazda Connected Services."""
def __init__(self):
"""Start the mazda config flow."""
<|body_0|>
async def async_step_user(self, user_input=None):
"""Handle the initial step."""
<|body_1|>
async def async_st... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MazdaConfigFlow:
"""Handle a config flow for Mazda Connected Services."""
def __init__(self):
"""Start the mazda config flow."""
self._reauth_entry = None
self._email = None
self._region = None
async def async_step_user(self, user_input=None):
"""Handle the in... | the_stack_v2_python_sparse | homeassistant/components/mazda/config_flow.py | home-assistant/core | train | 35,501 |
5bb7d761e08fd0c0af2d6a3bc672e97838a4f6e2 | [
"total = 0\nfor counter in SimpleCounterShard.objects.all():\n total += counter.count\nreturn total",
"index = random.randint(0, SimpleCounterShard.NUM_SHARDS - 1)\nshard_name = 'shard' + str(index)\ncounter = SimpleCounterShard.objects.get_or_create(pk=shard_name)[0]\ncounter.count += 1\ncounter.save()"
] | <|body_start_0|>
total = 0
for counter in SimpleCounterShard.objects.all():
total += counter.count
return total
<|end_body_0|>
<|body_start_1|>
index = random.randint(0, SimpleCounterShard.NUM_SHARDS - 1)
shard_name = 'shard' + str(index)
counter = SimpleCoun... | Shards for the counter | SimpleCounterShard | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SimpleCounterShard:
"""Shards for the counter"""
def get_count(cls):
"""Retrieve the value for a given sharded counter."""
<|body_0|>
def increment(cls):
"""Increment the value for a given sharded counter."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>... | stack_v2_sparse_classes_10k_train_005281 | 4,135 | no_license | [
{
"docstring": "Retrieve the value for a given sharded counter.",
"name": "get_count",
"signature": "def get_count(cls)"
},
{
"docstring": "Increment the value for a given sharded counter.",
"name": "increment",
"signature": "def increment(cls)"
}
] | 2 | stack_v2_sparse_classes_30k_val_000303 | Implement the Python class `SimpleCounterShard` described below.
Class description:
Shards for the counter
Method signatures and docstrings:
- def get_count(cls): Retrieve the value for a given sharded counter.
- def increment(cls): Increment the value for a given sharded counter. | Implement the Python class `SimpleCounterShard` described below.
Class description:
Shards for the counter
Method signatures and docstrings:
- def get_count(cls): Retrieve the value for a given sharded counter.
- def increment(cls): Increment the value for a given sharded counter.
<|skeleton|>
class SimpleCounterSha... | 2e3f1bdce124738e1bed2e648826ca819e0bcc57 | <|skeleton|>
class SimpleCounterShard:
"""Shards for the counter"""
def get_count(cls):
"""Retrieve the value for a given sharded counter."""
<|body_0|>
def increment(cls):
"""Increment the value for a given sharded counter."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SimpleCounterShard:
"""Shards for the counter"""
def get_count(cls):
"""Retrieve the value for a given sharded counter."""
total = 0
for counter in SimpleCounterShard.objects.all():
total += counter.count
return total
def increment(cls):
"""Increme... | the_stack_v2_python_sparse | sharded_counters/models.py | WAYbetter/waybetter | train | 2 |
190e934f86f9696378d34ce76759b0e595837599 | [
"while True:\n measurement = self.generate_message()\n measurement.save()\n print('Storing new measurement')\n time.sleep(10)",
"meter = Meter.objects.get_or_create(name='4530303237303030303130313334353136')[0]\nmeasurement = Measurement()\nmeasurement.meter = meter\nmeasurement.power_usage_current = ... | <|body_start_0|>
while True:
measurement = self.generate_message()
measurement.save()
print('Storing new measurement')
time.sleep(10)
<|end_body_0|>
<|body_start_1|>
meter = Meter.objects.get_or_create(name='4530303237303030303130313334353136')[0]
... | "Class responsible for generating fake measurements just for development and debugging purposes | Generator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Generator:
""""Class responsible for generating fake measurements just for development and debugging purposes"""
def start(self):
"""Starting the generator to create messages"""
<|body_0|>
def generate_message(self):
"""Genereates a new message"""
<|body_... | stack_v2_sparse_classes_10k_train_005282 | 1,335 | no_license | [
{
"docstring": "Starting the generator to create messages",
"name": "start",
"signature": "def start(self)"
},
{
"docstring": "Genereates a new message",
"name": "generate_message",
"signature": "def generate_message(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_004188 | Implement the Python class `Generator` described below.
Class description:
"Class responsible for generating fake measurements just for development and debugging purposes
Method signatures and docstrings:
- def start(self): Starting the generator to create messages
- def generate_message(self): Genereates a new messa... | Implement the Python class `Generator` described below.
Class description:
"Class responsible for generating fake measurements just for development and debugging purposes
Method signatures and docstrings:
- def start(self): Starting the generator to create messages
- def generate_message(self): Genereates a new messa... | 34f7c60d029b450e567150a8ed3714604a8504d0 | <|skeleton|>
class Generator:
""""Class responsible for generating fake measurements just for development and debugging purposes"""
def start(self):
"""Starting the generator to create messages"""
<|body_0|>
def generate_message(self):
"""Genereates a new message"""
<|body_... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Generator:
""""Class responsible for generating fake measurements just for development and debugging purposes"""
def start(self):
"""Starting the generator to create messages"""
while True:
measurement = self.generate_message()
measurement.save()
print(... | the_stack_v2_python_sparse | src/processor/generator.py | maarten-kieft/ASMP | train | 5 |
cdffdb6d8daa7dfcd99d078c0525ce41aeb678ac | [
"playlist_model = Playlist.get_by_id(int(playlist_id))\njson = []\nfor key in playlist_model.followers:\n youtify_user_model = db.get(key)\n json.append(get_youtify_user_struct(youtify_user_model))\nself.response.headers['Content-Type'] = 'application/json'\nself.response.out.write(simplejson.dumps(json))",
... | <|body_start_0|>
playlist_model = Playlist.get_by_id(int(playlist_id))
json = []
for key in playlist_model.followers:
youtify_user_model = db.get(key)
json.append(get_youtify_user_struct(youtify_user_model))
self.response.headers['Content-Type'] = 'application/jso... | PlaylistFollowersHandler | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PlaylistFollowersHandler:
def get(self, playlist_id):
"""Gets the list of users that follow a playlist"""
<|body_0|>
def post(self, playlist_id):
"""Follows a playlist"""
<|body_1|>
def delete(self, playlist_id):
"""Unfollows a playlist"""
... | stack_v2_sparse_classes_10k_train_005283 | 6,976 | permissive | [
{
"docstring": "Gets the list of users that follow a playlist",
"name": "get",
"signature": "def get(self, playlist_id)"
},
{
"docstring": "Follows a playlist",
"name": "post",
"signature": "def post(self, playlist_id)"
},
{
"docstring": "Unfollows a playlist",
"name": "delet... | 3 | stack_v2_sparse_classes_30k_val_000364 | Implement the Python class `PlaylistFollowersHandler` described below.
Class description:
Implement the PlaylistFollowersHandler class.
Method signatures and docstrings:
- def get(self, playlist_id): Gets the list of users that follow a playlist
- def post(self, playlist_id): Follows a playlist
- def delete(self, pla... | Implement the Python class `PlaylistFollowersHandler` described below.
Class description:
Implement the PlaylistFollowersHandler class.
Method signatures and docstrings:
- def get(self, playlist_id): Gets the list of users that follow a playlist
- def post(self, playlist_id): Follows a playlist
- def delete(self, pla... | 1855f242f15a9a66a8868ced849ddd77385426e7 | <|skeleton|>
class PlaylistFollowersHandler:
def get(self, playlist_id):
"""Gets the list of users that follow a playlist"""
<|body_0|>
def post(self, playlist_id):
"""Follows a playlist"""
<|body_1|>
def delete(self, playlist_id):
"""Unfollows a playlist"""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PlaylistFollowersHandler:
def get(self, playlist_id):
"""Gets the list of users that follow a playlist"""
playlist_model = Playlist.get_by_id(int(playlist_id))
json = []
for key in playlist_model.followers:
youtify_user_model = db.get(key)
json.append(ge... | the_stack_v2_python_sparse | playlists.py | blen2r/youtify | train | 0 | |
1b5caaf8edd93e3f28dbdee27db9e5d5714030ca | [
"super().__init__()\nself.dense_feature_extractor = dense_feature_extractor\nself.seg_classifier = seg_classifier\nself.changemixin = changemixin\nif inference_mode not in ['t1t2', 't2t1', 'mean']:\n raise ValueError(f'Unknown inference_mode: {inference_mode}')\nself.inference_mode = inference_mode",
"b, t, c,... | <|body_start_0|>
super().__init__()
self.dense_feature_extractor = dense_feature_extractor
self.seg_classifier = seg_classifier
self.changemixin = changemixin
if inference_mode not in ['t1t2', 't2t1', 'mean']:
raise ValueError(f'Unknown inference_mode: {inference_mode... | The base class of the network architecture of ChangeStar. ChangeStar is composed of an any segmentation model and a ChangeMixin module. This model is mainly used for binary/multi-class change detection under bitemporal supervision and single-temporal supervision. It features the property of segmentation architecture re... | ChangeStar | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ChangeStar:
"""The base class of the network architecture of ChangeStar. ChangeStar is composed of an any segmentation model and a ChangeMixin module. This model is mainly used for binary/multi-class change detection under bitemporal supervision and single-temporal supervision. It features the pr... | stack_v2_sparse_classes_10k_train_005284 | 7,715 | permissive | [
{
"docstring": "Initializes a new ChangeStar model. Args: dense_feature_extractor: module for dense feature extraction, typically a semantic segmentation model without semantic segmentation head. seg_classifier: semantic segmentation head, typically a convolutional layer followed by an upsampling layer. changem... | 2 | stack_v2_sparse_classes_30k_train_005989 | Implement the Python class `ChangeStar` described below.
Class description:
The base class of the network architecture of ChangeStar. ChangeStar is composed of an any segmentation model and a ChangeMixin module. This model is mainly used for binary/multi-class change detection under bitemporal supervision and single-t... | Implement the Python class `ChangeStar` described below.
Class description:
The base class of the network architecture of ChangeStar. ChangeStar is composed of an any segmentation model and a ChangeMixin module. This model is mainly used for binary/multi-class change detection under bitemporal supervision and single-t... | 29985861614b3b93f9ef5389469ebb98570de7dd | <|skeleton|>
class ChangeStar:
"""The base class of the network architecture of ChangeStar. ChangeStar is composed of an any segmentation model and a ChangeMixin module. This model is mainly used for binary/multi-class change detection under bitemporal supervision and single-temporal supervision. It features the pr... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ChangeStar:
"""The base class of the network architecture of ChangeStar. ChangeStar is composed of an any segmentation model and a ChangeMixin module. This model is mainly used for binary/multi-class change detection under bitemporal supervision and single-temporal supervision. It features the property of seg... | the_stack_v2_python_sparse | torchgeo/models/changestar.py | microsoft/torchgeo | train | 1,724 |
9b42a9cdebe9c8d70d467c6afe09e9f31d74560e | [
"self.continue_on_error = continue_on_error\nself.file_recovery_method = file_recovery_method\nself.filenames = filenames\nself.filter_ip_config = filter_ip_config\nself.is_file_based_volume_restore = is_file_based_volume_restore\nself.mount_disks_on_vm = mount_disks_on_vm\nself.name = name\nself.new_base_directory... | <|body_start_0|>
self.continue_on_error = continue_on_error
self.file_recovery_method = file_recovery_method
self.filenames = filenames
self.filter_ip_config = filter_ip_config
self.is_file_based_volume_restore = is_file_based_volume_restore
self.mount_disks_on_vm = mount... | Implementation of the 'RestoreFilesTaskRequest' model. Specifies information about a Restore Task that recovers files and folders. Attributes: continue_on_error (bool): Specifies if the Restore Task should continue even if the copy operation of some files and folders fails. If true, the Cohesity Cluster ignores intermi... | RestoreFilesTaskRequest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RestoreFilesTaskRequest:
"""Implementation of the 'RestoreFilesTaskRequest' model. Specifies information about a Restore Task that recovers files and folders. Attributes: continue_on_error (bool): Specifies if the Restore Task should continue even if the copy operation of some files and folders f... | stack_v2_sparse_classes_10k_train_005285 | 10,417 | permissive | [
{
"docstring": "Constructor for the RestoreFilesTaskRequest class",
"name": "__init__",
"signature": "def __init__(self, continue_on_error=None, file_recovery_method=None, filenames=None, filter_ip_config=None, is_file_based_volume_restore=None, mount_disks_on_vm=None, name=None, new_base_directory=None... | 2 | stack_v2_sparse_classes_30k_train_000177 | Implement the Python class `RestoreFilesTaskRequest` described below.
Class description:
Implementation of the 'RestoreFilesTaskRequest' model. Specifies information about a Restore Task that recovers files and folders. Attributes: continue_on_error (bool): Specifies if the Restore Task should continue even if the cop... | Implement the Python class `RestoreFilesTaskRequest` described below.
Class description:
Implementation of the 'RestoreFilesTaskRequest' model. Specifies information about a Restore Task that recovers files and folders. Attributes: continue_on_error (bool): Specifies if the Restore Task should continue even if the cop... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class RestoreFilesTaskRequest:
"""Implementation of the 'RestoreFilesTaskRequest' model. Specifies information about a Restore Task that recovers files and folders. Attributes: continue_on_error (bool): Specifies if the Restore Task should continue even if the copy operation of some files and folders f... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class RestoreFilesTaskRequest:
"""Implementation of the 'RestoreFilesTaskRequest' model. Specifies information about a Restore Task that recovers files and folders. Attributes: continue_on_error (bool): Specifies if the Restore Task should continue even if the copy operation of some files and folders fails. If true... | the_stack_v2_python_sparse | cohesity_management_sdk/models/restore_files_task_request.py | cohesity/management-sdk-python | train | 24 |
6635b58e3e193db9c876f0c944948c37beeaaaef | [
"import bisect\na = sorted(A)\nresult = []\nfor b in B:\n p = bisect.bisect(a, b)\n if p < len(a):\n result.append(a[p])\n a.pop(p)\n else:\n result.append(a[0])\n a.pop(0)\nreturn result",
"l = len(A)\nres = [0] * l\nidx = range(l)\nidx.sort(key=lambda x: B[x])\nA.sort()\nlef... | <|body_start_0|>
import bisect
a = sorted(A)
result = []
for b in B:
p = bisect.bisect(a, b)
if p < len(a):
result.append(a[p])
a.pop(p)
else:
result.append(a[0])
a.pop(0)
return r... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def advantageCount(self, A, B):
""":type A: List[int] :type B: List[int] :rtype: List[int] 316 ms"""
<|body_0|>
def advantageCount_1(self, A, B):
""":type A: List[int] :type B: List[int] :rtype: List[int] 220ms"""
<|body_1|>
<|end_skeleton|>
<|bod... | stack_v2_sparse_classes_10k_train_005286 | 1,670 | no_license | [
{
"docstring": ":type A: List[int] :type B: List[int] :rtype: List[int] 316 ms",
"name": "advantageCount",
"signature": "def advantageCount(self, A, B)"
},
{
"docstring": ":type A: List[int] :type B: List[int] :rtype: List[int] 220ms",
"name": "advantageCount_1",
"signature": "def advant... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def advantageCount(self, A, B): :type A: List[int] :type B: List[int] :rtype: List[int] 316 ms
- def advantageCount_1(self, A, B): :type A: List[int] :type B: List[int] :rtype: L... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def advantageCount(self, A, B): :type A: List[int] :type B: List[int] :rtype: List[int] 316 ms
- def advantageCount_1(self, A, B): :type A: List[int] :type B: List[int] :rtype: L... | 679a2b246b8b6bb7fc55ed1c8096d3047d6d4461 | <|skeleton|>
class Solution:
def advantageCount(self, A, B):
""":type A: List[int] :type B: List[int] :rtype: List[int] 316 ms"""
<|body_0|>
def advantageCount_1(self, A, B):
""":type A: List[int] :type B: List[int] :rtype: List[int] 220ms"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def advantageCount(self, A, B):
""":type A: List[int] :type B: List[int] :rtype: List[int] 316 ms"""
import bisect
a = sorted(A)
result = []
for b in B:
p = bisect.bisect(a, b)
if p < len(a):
result.append(a[p])
... | the_stack_v2_python_sparse | AdvantageShuffle_MID_870.py | 953250587/leetcode-python | train | 2 | |
a37c02a80373a12f9bf9c3db57155b7b974f2f49 | [
"super().__init__(name)\nself.model = model\nself.alphabet = alphabet",
"one_hots = np.array([s_utils.string_to_one_hot(seq, self.alphabet) for seq in sequences])\nflattened = one_hots.reshape(one_hots.shape[0], one_hots.shape[1] * one_hots.shape[2])\nself.model.fit(flattened, labels)"
] | <|body_start_0|>
super().__init__(name)
self.model = model
self.alphabet = alphabet
<|end_body_0|>
<|body_start_1|>
one_hots = np.array([s_utils.string_to_one_hot(seq, self.alphabet) for seq in sequences])
flattened = one_hots.reshape(one_hots.shape[0], one_hots.shape[1] * one_h... | Base sklearn model wrapper. | SklearnModel | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SklearnModel:
"""Base sklearn model wrapper."""
def __init__(self, model, alphabet, name):
"""Args: model: sklearn model to wrap. alphabet: Alphabet string. name: Human-readable short model descriptipon (for logging)."""
<|body_0|>
def train(self, sequences, labels):
... | stack_v2_sparse_classes_10k_train_005287 | 2,860 | permissive | [
{
"docstring": "Args: model: sklearn model to wrap. alphabet: Alphabet string. name: Human-readable short model descriptipon (for logging).",
"name": "__init__",
"signature": "def __init__(self, model, alphabet, name)"
},
{
"docstring": "Flatten one-hot sequences and train model using `model.fit... | 2 | stack_v2_sparse_classes_30k_train_001759 | Implement the Python class `SklearnModel` described below.
Class description:
Base sklearn model wrapper.
Method signatures and docstrings:
- def __init__(self, model, alphabet, name): Args: model: sklearn model to wrap. alphabet: Alphabet string. name: Human-readable short model descriptipon (for logging).
- def tra... | Implement the Python class `SklearnModel` described below.
Class description:
Base sklearn model wrapper.
Method signatures and docstrings:
- def __init__(self, model, alphabet, name): Args: model: sklearn model to wrap. alphabet: Alphabet string. name: Human-readable short model descriptipon (for logging).
- def tra... | 744e792456d93e8c48fc58220689c0b4cff6ded9 | <|skeleton|>
class SklearnModel:
"""Base sklearn model wrapper."""
def __init__(self, model, alphabet, name):
"""Args: model: sklearn model to wrap. alphabet: Alphabet string. name: Human-readable short model descriptipon (for logging)."""
<|body_0|>
def train(self, sequences, labels):
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SklearnModel:
"""Base sklearn model wrapper."""
def __init__(self, model, alphabet, name):
"""Args: model: sklearn model to wrap. alphabet: Alphabet string. name: Human-readable short model descriptipon (for logging)."""
super().__init__(name)
self.model = model
self.alpha... | the_stack_v2_python_sparse | flexs/baselines/models/sklearn_models.py | jonshao/FLEXS | train | 0 |
80f395b024f362925ada43334b415ffb18d71c11 | [
"command_name = command_node.get('CommandName')\ncommand_id = command_node.get('CommandId')\ncommand_params = defaultdict(list)\nnamespace = XMLHelper.get_node_namespace(command_node)\nparameters_node = command_node.find(namespace + 'Parameters')\nif parameters_node is not None:\n for param_node in parameters_no... | <|body_start_0|>
command_name = command_node.get('CommandName')
command_id = command_node.get('CommandId')
command_params = defaultdict(list)
namespace = XMLHelper.get_node_namespace(command_node)
parameters_node = command_node.find(namespace + 'Parameters')
if parameters... | Parse request data and build command requests. | RequestsParser | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RequestsParser:
"""Parse request data and build command requests."""
def _build_command_instance(command_node: Element) -> CommandRequest:
"""Build command instance for command node."""
<|body_0|>
def parse_request_commands(xml_request: str) -> list:
"""Parse xml... | stack_v2_sparse_classes_10k_train_005288 | 1,428 | no_license | [
{
"docstring": "Build command instance for command node.",
"name": "_build_command_instance",
"signature": "def _build_command_instance(command_node: Element) -> CommandRequest"
},
{
"docstring": "Parse xml request and create command instances.",
"name": "parse_request_commands",
"signat... | 2 | stack_v2_sparse_classes_30k_train_001817 | Implement the Python class `RequestsParser` described below.
Class description:
Parse request data and build command requests.
Method signatures and docstrings:
- def _build_command_instance(command_node: Element) -> CommandRequest: Build command instance for command node.
- def parse_request_commands(xml_request: st... | Implement the Python class `RequestsParser` described below.
Class description:
Parse request data and build command requests.
Method signatures and docstrings:
- def _build_command_instance(command_node: Element) -> CommandRequest: Build command instance for command node.
- def parse_request_commands(xml_request: st... | 82562665834908294136bbe8e7bc46da1a21b8e2 | <|skeleton|>
class RequestsParser:
"""Parse request data and build command requests."""
def _build_command_instance(command_node: Element) -> CommandRequest:
"""Build command instance for command node."""
<|body_0|>
def parse_request_commands(xml_request: str) -> list:
"""Parse xml... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class RequestsParser:
"""Parse request data and build command requests."""
def _build_command_instance(command_node: Element) -> CommandRequest:
"""Build command instance for command node."""
command_name = command_node.get('CommandName')
command_id = command_node.get('CommandId')
... | the_stack_v2_python_sparse | cloudshell/layer_one/core/request/requests_parser.py | QualiSystems/cloudshell-L1-networking-core | train | 1 |
e251d622490df247303aaa275371321013e0363f | [
"n1, n2 = (len(self), len(other))\nv1, v2 = (self._data.var(), other._data.var())\nx1, x2 = (self._data.mean(), other._data.mean())\ns = (((n1 - 1) * v1 + (n2 - 1) * v2) / (n1 + n2 - 2)) ** 0.5\nreturn (x1 - x2) / s",
"dists: Dict[str, DCDM] = {category: self.filter_to(categorical.keep(category)).rename(category)... | <|body_start_0|>
n1, n2 = (len(self), len(other))
v1, v2 = (self._data.var(), other._data.var())
x1, x2 = (self._data.mean(), other._data.mean())
s = (((n1 - 1) * v1 + (n2 - 1) * v2) / (n1 + n2 - 2)) ** 0.5
return (x1 - x2) / s
<|end_body_0|>
<|body_start_1|>
dists: Dict... | DataCohensDMixin | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DataCohensDMixin:
def cohens_d(self: DCDM, other: DCDM) -> float:
"""Calculate the Cohen's d standardized difference of means between self and other. https://en.wikipedia.org/wiki/Effect_size#Cohen's_d"""
<|body_0|>
def conditional_cohens_d(self: DCDM, categorical: DataCateg... | stack_v2_sparse_classes_10k_train_005289 | 2,575 | permissive | [
{
"docstring": "Calculate the Cohen's d standardized difference of means between self and other. https://en.wikipedia.org/wiki/Effect_size#Cohen's_d",
"name": "cohens_d",
"signature": "def cohens_d(self: DCDM, other: DCDM) -> float"
},
{
"docstring": "Return a matrix of the Cohen's d of the Rati... | 2 | stack_v2_sparse_classes_30k_train_002460 | Implement the Python class `DataCohensDMixin` described below.
Class description:
Implement the DataCohensDMixin class.
Method signatures and docstrings:
- def cohens_d(self: DCDM, other: DCDM) -> float: Calculate the Cohen's d standardized difference of means between self and other. https://en.wikipedia.org/wiki/Eff... | Implement the Python class `DataCohensDMixin` described below.
Class description:
Implement the DataCohensDMixin class.
Method signatures and docstrings:
- def cohens_d(self: DCDM, other: DCDM) -> float: Calculate the Cohen's d standardized difference of means between self and other. https://en.wikipedia.org/wiki/Eff... | ff3f5434d3da0d46b127b02cf733699e5a43c904 | <|skeleton|>
class DataCohensDMixin:
def cohens_d(self: DCDM, other: DCDM) -> float:
"""Calculate the Cohen's d standardized difference of means between self and other. https://en.wikipedia.org/wiki/Effect_size#Cohen's_d"""
<|body_0|>
def conditional_cohens_d(self: DCDM, categorical: DataCateg... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class DataCohensDMixin:
def cohens_d(self: DCDM, other: DCDM) -> float:
"""Calculate the Cohen's d standardized difference of means between self and other. https://en.wikipedia.org/wiki/Effect_size#Cohen's_d"""
n1, n2 = (len(self), len(other))
v1, v2 = (self._data.var(), other._data.var())
... | the_stack_v2_python_sparse | probability/distributions/mixins/data/data_comparison_mixins.py | vahndi/probability | train | 3 | |
e0c26bfdb27fee2eb9eb2b33840c0005e36987d1 | [
"self.batched_inputs = inputs\nproposals_boxes = proposals[PD_BOXES]\nif self.is_training:\n proposals = self.label_and_sample_proposals(inputs, proposals_boxes)\nfeatures_list = [features[f] for f in self.in_features]\nimg_size = get_img_size_from_batched_inputs(inputs)\nif self.is_training:\n pred_instances... | <|body_start_0|>
self.batched_inputs = inputs
proposals_boxes = proposals[PD_BOXES]
if self.is_training:
proposals = self.label_and_sample_proposals(inputs, proposals_boxes)
features_list = [features[f] for f in self.in_features]
img_size = get_img_size_from_batched_i... | RepeatableROIHeads | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RepeatableROIHeads:
def forward(self, inputs, features, proposals: ProposalsData):
"""See :class:`ROIHeads.forward`."""
<|body_0|>
def forward_with_given_boxes(self, inputs, features, instances, img_size):
"""Use the given boxes in `instances` to produce other (non-b... | stack_v2_sparse_classes_10k_train_005290 | 6,801 | permissive | [
{
"docstring": "See :class:`ROIHeads.forward`.",
"name": "forward",
"signature": "def forward(self, inputs, features, proposals: ProposalsData)"
},
{
"docstring": "Use the given boxes in `instances` to produce other (non-box) per-ROI outputs. This is useful for downstream tasks where a box is kn... | 3 | null | Implement the Python class `RepeatableROIHeads` described below.
Class description:
Implement the RepeatableROIHeads class.
Method signatures and docstrings:
- def forward(self, inputs, features, proposals: ProposalsData): See :class:`ROIHeads.forward`.
- def forward_with_given_boxes(self, inputs, features, instances... | Implement the Python class `RepeatableROIHeads` described below.
Class description:
Implement the RepeatableROIHeads class.
Method signatures and docstrings:
- def forward(self, inputs, features, proposals: ProposalsData): See :class:`ROIHeads.forward`.
- def forward_with_given_boxes(self, inputs, features, instances... | 8fbf060088816cd1a366d7cbd5dfe1a0e00f8d79 | <|skeleton|>
class RepeatableROIHeads:
def forward(self, inputs, features, proposals: ProposalsData):
"""See :class:`ROIHeads.forward`."""
<|body_0|>
def forward_with_given_boxes(self, inputs, features, instances, img_size):
"""Use the given boxes in `instances` to produce other (non-b... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class RepeatableROIHeads:
def forward(self, inputs, features, proposals: ProposalsData):
"""See :class:`ROIHeads.forward`."""
self.batched_inputs = inputs
proposals_boxes = proposals[PD_BOXES]
if self.is_training:
proposals = self.label_and_sample_proposals(inputs, propos... | the_stack_v2_python_sparse | object_detection2/modeling/roi_heads/repeatable_roi_heads.py | seantangtao/wml | train | 0 | |
cc75f4d7e6e06f88bf84376aed119aee8edd272d | [
"files = self._get_filesfixedforvulnerability()\nids = self._get_missedvulnerabilityreviewids(files)\nReview.objects.filter(id__in=ids).update(missed_vulnerability=True)\nreturn len(ids)",
"reviews = set()\nfor vulnerability in Vulnerability.objects.all():\n for bug in vulnerability.bugs.all():\n for re... | <|body_start_0|>
files = self._get_filesfixedforvulnerability()
ids = self._get_missedvulnerabilityreviewids(files)
Review.objects.filter(id__in=ids).update(missed_vulnerability=True)
return len(ids)
<|end_body_0|>
<|body_start_1|>
reviews = set()
for vulnerability in Vu... | Implements tagger object. | MissedVulnerabilityTagger | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MissedVulnerabilityTagger:
"""Implements tagger object."""
def _tag(self):
"""Tag all of the reviews that missed a vulnerability."""
<|body_0|>
def _get_vulnerabilityfixingreviews(self):
"""Returns a list of reviews that fixed a vulnerability."""
<|body_1... | stack_v2_sparse_classes_10k_train_005291 | 2,297 | no_license | [
{
"docstring": "Tag all of the reviews that missed a vulnerability.",
"name": "_tag",
"signature": "def _tag(self)"
},
{
"docstring": "Returns a list of reviews that fixed a vulnerability.",
"name": "_get_vulnerabilityfixingreviews",
"signature": "def _get_vulnerabilityfixingreviews(self... | 5 | stack_v2_sparse_classes_30k_test_000199 | Implement the Python class `MissedVulnerabilityTagger` described below.
Class description:
Implements tagger object.
Method signatures and docstrings:
- def _tag(self): Tag all of the reviews that missed a vulnerability.
- def _get_vulnerabilityfixingreviews(self): Returns a list of reviews that fixed a vulnerability... | Implement the Python class `MissedVulnerabilityTagger` described below.
Class description:
Implements tagger object.
Method signatures and docstrings:
- def _tag(self): Tag all of the reviews that missed a vulnerability.
- def _get_vulnerabilityfixingreviews(self): Returns a list of reviews that fixed a vulnerability... | b027a5d7407043b6541e2aa02704a7239f109485 | <|skeleton|>
class MissedVulnerabilityTagger:
"""Implements tagger object."""
def _tag(self):
"""Tag all of the reviews that missed a vulnerability."""
<|body_0|>
def _get_vulnerabilityfixingreviews(self):
"""Returns a list of reviews that fixed a vulnerability."""
<|body_1... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MissedVulnerabilityTagger:
"""Implements tagger object."""
def _tag(self):
"""Tag all of the reviews that missed a vulnerability."""
files = self._get_filesfixedforvulnerability()
ids = self._get_missedvulnerabilityreviewids(files)
Review.objects.filter(id__in=ids).update(... | the_stack_v2_python_sparse | app/lib/taggers/missedvulnerability.py | andymeneely/sira-nlp | train | 1 |
e4aac5a626b90c096618d91e89663a6019c9bc4c | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn SecurityResource()",
"from .security_resource_type import SecurityResourceType\nfrom .security_resource_type import SecurityResourceType\nfields: Dict[str, Callable[[Any], None]] = {'@odata.type': lambda n: setattr(self, 'odata_type', ... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return SecurityResource()
<|end_body_0|>
<|body_start_1|>
from .security_resource_type import SecurityResourceType
from .security_resource_type import SecurityResourceType
fields: Dict[... | SecurityResource | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SecurityResource:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> SecurityResource:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object R... | stack_v2_sparse_classes_10k_train_005292 | 3,039 | permissive | [
{
"docstring": "Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: SecurityResource",
"name": "create_from_discriminator_value",
"signature": "def create_from_discriminator_va... | 3 | stack_v2_sparse_classes_30k_val_000317 | Implement the Python class `SecurityResource` described below.
Class description:
Implement the SecurityResource class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> SecurityResource: Creates a new instance of the appropriate class based on discrimina... | Implement the Python class `SecurityResource` described below.
Class description:
Implement the SecurityResource class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> SecurityResource: Creates a new instance of the appropriate class based on discrimina... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class SecurityResource:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> SecurityResource:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object R... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SecurityResource:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> SecurityResource:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: Securi... | the_stack_v2_python_sparse | msgraph/generated/models/security_resource.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
a3787bdaf667f3b1b2bb9707a64752739d6307af | [
"dict = {}\nfor i in strs:\n sort_s = ''.join((lambda x: (x.sort(), x)[1])(list(i)))\n if not dict:\n dict[sort_s] = [i]\n elif sort_s in dict.keys():\n dict[sort_s].append(i)\n else:\n dict[sort_s] = [i]\nreturn dict.values()",
"result_dict = collections.defaultdict(list)\nfor s ... | <|body_start_0|>
dict = {}
for i in strs:
sort_s = ''.join((lambda x: (x.sort(), x)[1])(list(i)))
if not dict:
dict[sort_s] = [i]
elif sort_s in dict.keys():
dict[sort_s].append(i)
else:
dict[sort_s] = [i]
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def groupAnagrams0(self, strs):
""":type strs: List[str] :rtype: List[List[str]] 解题思路: 定义一个字典: key为字符串排序后的字符串; value为原始字符串"""
<|body_0|>
def groupAnagrams1(self, strs):
"""使用python的collections模块实现, 简化代码 思路与groupAnagrams0 相同"""
<|body_1|>
<|end_skel... | stack_v2_sparse_classes_10k_train_005293 | 2,115 | permissive | [
{
"docstring": ":type strs: List[str] :rtype: List[List[str]] 解题思路: 定义一个字典: key为字符串排序后的字符串; value为原始字符串",
"name": "groupAnagrams0",
"signature": "def groupAnagrams0(self, strs)"
},
{
"docstring": "使用python的collections模块实现, 简化代码 思路与groupAnagrams0 相同",
"name": "groupAnagrams1",
"signature"... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def groupAnagrams0(self, strs): :type strs: List[str] :rtype: List[List[str]] 解题思路: 定义一个字典: key为字符串排序后的字符串; value为原始字符串
- def groupAnagrams1(self, strs): 使用python的collections模块实现... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def groupAnagrams0(self, strs): :type strs: List[str] :rtype: List[List[str]] 解题思路: 定义一个字典: key为字符串排序后的字符串; value为原始字符串
- def groupAnagrams1(self, strs): 使用python的collections模块实现... | 60e9ef1051a1d0441ab1c5484a51ab77a306bf5b | <|skeleton|>
class Solution:
def groupAnagrams0(self, strs):
""":type strs: List[str] :rtype: List[List[str]] 解题思路: 定义一个字典: key为字符串排序后的字符串; value为原始字符串"""
<|body_0|>
def groupAnagrams1(self, strs):
"""使用python的collections模块实现, 简化代码 思路与groupAnagrams0 相同"""
<|body_1|>
<|end_skel... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def groupAnagrams0(self, strs):
""":type strs: List[str] :rtype: List[List[str]] 解题思路: 定义一个字典: key为字符串排序后的字符串; value为原始字符串"""
dict = {}
for i in strs:
sort_s = ''.join((lambda x: (x.sort(), x)[1])(list(i)))
if not dict:
dict[sort_s] = [... | the_stack_v2_python_sparse | Week 2/id_710/LeetCode_49_710.py | chenlei65368/algorithm004-05 | train | 1 | |
635c6cf359ae76df657825622c906038cf48c194 | [
"if self.field:\n return f'Top results for \"{self.field:s}\"'\nreturn 'Top results for an unknown field'",
"self.field = field\nformatted_field_name = self.format_field_by_type(field)\nencoding = {'x': {'field': field, 'type': 'nominal', 'sort': {'op': 'sum', 'field': order_field, 'order': 'descending'}}, 'y'... | <|body_start_0|>
if self.field:
return f'Top results for "{self.field:s}"'
return 'Top results for an unknown field'
<|end_body_0|>
<|body_start_1|>
self.field = field
formatted_field_name = self.format_field_by_type(field)
encoding = {'x': {'field': field, 'type': '... | Terms Bucket Aggregation. | TermsAggregation | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TermsAggregation:
"""Terms Bucket Aggregation."""
def chart_title(self):
"""Returns a title for the chart."""
<|body_0|>
def run(self, field, limit=10, supported_charts='table', start_time='', end_time='', order_field='count'):
"""Run the aggregation. Args: field... | stack_v2_sparse_classes_10k_train_005294 | 5,187 | permissive | [
{
"docstring": "Returns a title for the chart.",
"name": "chart_title",
"signature": "def chart_title(self)"
},
{
"docstring": "Run the aggregation. Args: field: What field to aggregate on. limit: How many buckets to return. supported_charts: Chart type to render. Defaults to table. start_time: ... | 2 | null | Implement the Python class `TermsAggregation` described below.
Class description:
Terms Bucket Aggregation.
Method signatures and docstrings:
- def chart_title(self): Returns a title for the chart.
- def run(self, field, limit=10, supported_charts='table', start_time='', end_time='', order_field='count'): Run the agg... | Implement the Python class `TermsAggregation` described below.
Class description:
Terms Bucket Aggregation.
Method signatures and docstrings:
- def chart_title(self): Returns a title for the chart.
- def run(self, field, limit=10, supported_charts='table', start_time='', end_time='', order_field='count'): Run the agg... | 24f471b58ca4a87cb053961b5f05c07a544ca7b8 | <|skeleton|>
class TermsAggregation:
"""Terms Bucket Aggregation."""
def chart_title(self):
"""Returns a title for the chart."""
<|body_0|>
def run(self, field, limit=10, supported_charts='table', start_time='', end_time='', order_field='count'):
"""Run the aggregation. Args: field... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TermsAggregation:
"""Terms Bucket Aggregation."""
def chart_title(self):
"""Returns a title for the chart."""
if self.field:
return f'Top results for "{self.field:s}"'
return 'Top results for an unknown field'
def run(self, field, limit=10, supported_charts='table... | the_stack_v2_python_sparse | timesketch/lib/aggregators/bucket.py | google/timesketch | train | 2,263 |
79c6fd96ee3fa40e17e393494783294e2869252f | [
"for name, working_partitions in cls.data_source.working_partitions.items():\n try:\n partition = partitions[name]\n except KeyError:\n raise ValueError(f\"{cls.__name__} is missing required '{name}' field.\")\n if not partitions[name]:\n partition = working_partitions\n partitions_... | <|body_start_0|>
for name, working_partitions in cls.data_source.working_partitions.items():
try:
partition = partitions[name]
except KeyError:
raise ValueError(f"{cls.__name__} is missing required '{name}' field.")
if not partitions[name]:
... | An abstract pydantic model for generic datasets. Each dataset must specify working partitions. A dataset can have an arbitrary number of partitions. Args: disabled: if true, skip processing this dataset. | GenericDatasetSettings | [
"CC-BY-4.0",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GenericDatasetSettings:
"""An abstract pydantic model for generic datasets. Each dataset must specify working partitions. A dataset can have an arbitrary number of partitions. Args: disabled: if true, skip processing this dataset."""
def validate_partitions(cls, partitions):
"""Valid... | stack_v2_sparse_classes_10k_train_005295 | 24,804 | permissive | [
{
"docstring": "Validate the requested data partitions. Check that all the partitions defined in the ``working_partitions`` of the associated ``data_source`` (e.g. years or states) have been assigned in the definition of the class, and that the requested values are a subset of the allowable values defined by th... | 2 | stack_v2_sparse_classes_30k_train_004880 | Implement the Python class `GenericDatasetSettings` described below.
Class description:
An abstract pydantic model for generic datasets. Each dataset must specify working partitions. A dataset can have an arbitrary number of partitions. Args: disabled: if true, skip processing this dataset.
Method signatures and docs... | Implement the Python class `GenericDatasetSettings` described below.
Class description:
An abstract pydantic model for generic datasets. Each dataset must specify working partitions. A dataset can have an arbitrary number of partitions. Args: disabled: if true, skip processing this dataset.
Method signatures and docs... | 6afae8aade053408f23ac4332d5cbb438ab72dc6 | <|skeleton|>
class GenericDatasetSettings:
"""An abstract pydantic model for generic datasets. Each dataset must specify working partitions. A dataset can have an arbitrary number of partitions. Args: disabled: if true, skip processing this dataset."""
def validate_partitions(cls, partitions):
"""Valid... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class GenericDatasetSettings:
"""An abstract pydantic model for generic datasets. Each dataset must specify working partitions. A dataset can have an arbitrary number of partitions. Args: disabled: if true, skip processing this dataset."""
def validate_partitions(cls, partitions):
"""Validate the reque... | the_stack_v2_python_sparse | src/pudl/settings.py | catalyst-cooperative/pudl | train | 382 |
06054eac6355f83806d5f06d5a5287a5724efae8 | [
"if not heights:\n return 0\nstack = []\nmax_area, index = (0, 0)\nlength = len(heights)\nwhile index <= length:\n if not stack or (index < length and heights[index] >= heights[stack[-1]]):\n stack.append(index)\n index += 1\n else:\n old_index = stack.pop()\n width = index if l... | <|body_start_0|>
if not heights:
return 0
stack = []
max_area, index = (0, 0)
length = len(heights)
while index <= length:
if not stack or (index < length and heights[index] >= heights[stack[-1]]):
stack.append(index)
index ... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def largest_rectangle_histogram(self, heights: List[int]) -> int:
"""计算最大矩形长度 Args: heights: 数组长度 Returns: 矩形最大面积"""
<|body_0|>
def largest_rectangle_histogram2(self, heights: List[int]) -> int:
"""计算最大矩形长度 Args: heights: 数组长度 Returns: 矩形最大面积"""
<|b... | stack_v2_sparse_classes_10k_train_005296 | 2,962 | permissive | [
{
"docstring": "计算最大矩形长度 Args: heights: 数组长度 Returns: 矩形最大面积",
"name": "largest_rectangle_histogram",
"signature": "def largest_rectangle_histogram(self, heights: List[int]) -> int"
},
{
"docstring": "计算最大矩形长度 Args: heights: 数组长度 Returns: 矩形最大面积",
"name": "largest_rectangle_histogram2",
... | 2 | stack_v2_sparse_classes_30k_train_005975 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def largest_rectangle_histogram(self, heights: List[int]) -> int: 计算最大矩形长度 Args: heights: 数组长度 Returns: 矩形最大面积
- def largest_rectangle_histogram2(self, heights: List[int]) -> int... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def largest_rectangle_histogram(self, heights: List[int]) -> int: 计算最大矩形长度 Args: heights: 数组长度 Returns: 矩形最大面积
- def largest_rectangle_histogram2(self, heights: List[int]) -> int... | 50f35eef6a0ad63173efed10df3c835b1dceaa3f | <|skeleton|>
class Solution:
def largest_rectangle_histogram(self, heights: List[int]) -> int:
"""计算最大矩形长度 Args: heights: 数组长度 Returns: 矩形最大面积"""
<|body_0|>
def largest_rectangle_histogram2(self, heights: List[int]) -> int:
"""计算最大矩形长度 Args: heights: 数组长度 Returns: 矩形最大面积"""
<|b... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def largest_rectangle_histogram(self, heights: List[int]) -> int:
"""计算最大矩形长度 Args: heights: 数组长度 Returns: 矩形最大面积"""
if not heights:
return 0
stack = []
max_area, index = (0, 0)
length = len(heights)
while index <= length:
if no... | the_stack_v2_python_sparse | src/leetcodepython/array/largest_rectangle_histogram_84.py | zhangyu345293721/leetcode | train | 101 | |
ebfe3525b7dd4f9ec0f5ee73aa2f050d288677ca | [
"assert all((len(c) == 2 and isinstance(c[0], str) and isinstance(c[1], int) for c in columns)), columns\nself.use_cr_only = True\nself.unfinished_commands = set()\nself.start = time.time()\nself._last_printed_line = ''\nself._columns = [c[1] for c in columns]\nself._columns_lookup = dict(((c[0], i) for i, c in enu... | <|body_start_0|>
assert all((len(c) == 2 and isinstance(c[0], str) and isinstance(c[1], int) for c in columns)), columns
self.use_cr_only = True
self.unfinished_commands = set()
self.start = time.time()
self._last_printed_line = ''
self._columns = [c[1] for c in columns]
... | Prints progress and accepts updates thread-safely. | Progress | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Progress:
"""Prints progress and accepts updates thread-safely."""
def __init__(self, columns):
"""Creates a Progress bar that will updates asynchronously from the worker threads. Arguments: columns: list of tuple(name, initialvalue), defines both the number of columns and their init... | stack_v2_sparse_classes_10k_train_005297 | 27,770 | permissive | [
{
"docstring": "Creates a Progress bar that will updates asynchronously from the worker threads. Arguments: columns: list of tuple(name, initialvalue), defines both the number of columns and their initial values.",
"name": "__init__",
"signature": "def __init__(self, columns)"
},
{
"docstring": ... | 5 | null | Implement the Python class `Progress` described below.
Class description:
Prints progress and accepts updates thread-safely.
Method signatures and docstrings:
- def __init__(self, columns): Creates a Progress bar that will updates asynchronously from the worker threads. Arguments: columns: list of tuple(name, initial... | Implement the Python class `Progress` described below.
Class description:
Prints progress and accepts updates thread-safely.
Method signatures and docstrings:
- def __init__(self, columns): Creates a Progress bar that will updates asynchronously from the worker threads. Arguments: columns: list of tuple(name, initial... | 10cc5fdcca53e2a1690867acbe6fce099273f092 | <|skeleton|>
class Progress:
"""Prints progress and accepts updates thread-safely."""
def __init__(self, columns):
"""Creates a Progress bar that will updates asynchronously from the worker threads. Arguments: columns: list of tuple(name, initialvalue), defines both the number of columns and their init... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Progress:
"""Prints progress and accepts updates thread-safely."""
def __init__(self, columns):
"""Creates a Progress bar that will updates asynchronously from the worker threads. Arguments: columns: list of tuple(name, initialvalue), defines both the number of columns and their initial values.""... | the_stack_v2_python_sparse | client/utils/threading_utils.py | luci/luci-py | train | 84 |
cb5800570c14ff3897a8a6e66f3b9a5e44c616cb | [
"N = len(nums)\ns, l, ans = (sum(nums), 1, float('inf'))\nnums.insert(0, 0)\nfor r in range(N + 1):\n s -= nums[r]\n while l < r and s < x:\n s += nums[l]\n l += 1\n if s == x:\n ans = min(ans, l - 1 + N - r)\nreturn ans if ans != float('inf') else -1",
"N = len(nums)\nd = {0: -1}\ns... | <|body_start_0|>
N = len(nums)
s, l, ans = (sum(nums), 1, float('inf'))
nums.insert(0, 0)
for r in range(N + 1):
s -= nums[r]
while l < r and s < x:
s += nums[l]
l += 1
if s == x:
ans = min(ans, l - 1 + N... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def minOperations(self, nums: List[int], x: int) -> int:
"""Sliding Window O(N) / O(1)"""
<|body_0|>
def minOperations1(self, nums: List[int], x: int) -> int:
"""Prefix Sum 왼쪽의 합을 미리 저장해놓고, 오른쪽에서 시작해서 최소의 길이를 구함. O(N) / O(N)"""
<|body_1|>
<|end_ske... | stack_v2_sparse_classes_10k_train_005298 | 1,322 | no_license | [
{
"docstring": "Sliding Window O(N) / O(1)",
"name": "minOperations",
"signature": "def minOperations(self, nums: List[int], x: int) -> int"
},
{
"docstring": "Prefix Sum 왼쪽의 합을 미리 저장해놓고, 오른쪽에서 시작해서 최소의 길이를 구함. O(N) / O(N)",
"name": "minOperations1",
"signature": "def minOperations1(self... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minOperations(self, nums: List[int], x: int) -> int: Sliding Window O(N) / O(1)
- def minOperations1(self, nums: List[int], x: int) -> int: Prefix Sum 왼쪽의 합을 미리 저장해놓고, 오른쪽에서 ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minOperations(self, nums: List[int], x: int) -> int: Sliding Window O(N) / O(1)
- def minOperations1(self, nums: List[int], x: int) -> int: Prefix Sum 왼쪽의 합을 미리 저장해놓고, 오른쪽에서 ... | c26aef2a59e5cc2d9b0658b9c7386a43267ff8a1 | <|skeleton|>
class Solution:
def minOperations(self, nums: List[int], x: int) -> int:
"""Sliding Window O(N) / O(1)"""
<|body_0|>
def minOperations1(self, nums: List[int], x: int) -> int:
"""Prefix Sum 왼쪽의 합을 미리 저장해놓고, 오른쪽에서 시작해서 최소의 길이를 구함. O(N) / O(N)"""
<|body_1|>
<|end_ske... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def minOperations(self, nums: List[int], x: int) -> int:
"""Sliding Window O(N) / O(1)"""
N = len(nums)
s, l, ans = (sum(nums), 1, float('inf'))
nums.insert(0, 0)
for r in range(N + 1):
s -= nums[r]
while l < r and s < x:
... | the_stack_v2_python_sparse | Leetcode/1658.py | hanwgyu/algorithm_problem_solving | train | 5 | |
2bfd235494d5c9710a4c67cd610b5bced84ba325 | [
"LOG.debug('Setting register {:#0x} to value {}'.format(p_register, p_values))\nBuildCommand.write_register_command(p_controller_obj, p_register, p_values)\npass",
"l_val = bytearray(1)\nl_val[0] = 3\nself.set_register_value(255, 112, l_val)"
] | <|body_start_0|>
LOG.debug('Setting register {:#0x} to value {}'.format(p_register, p_values))
BuildCommand.write_register_command(p_controller_obj, p_register, p_values)
pass
<|end_body_0|>
<|body_start_1|>
l_val = bytearray(1)
l_val[0] = 3
self.set_register_value(255, ... | CreateCommands | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CreateCommands:
def set_register_value(self, p_controller_obj, p_register, p_values):
"""Set one of the device's registers."""
<|body_0|>
def set_pim_mode(self):
"""Set the PIM operating mode: Page 6 of UPB Powerline Interface Module (PIM) Description Version 1.6 The... | stack_v2_sparse_classes_10k_train_005299 | 17,549 | permissive | [
{
"docstring": "Set one of the device's registers.",
"name": "set_register_value",
"signature": "def set_register_value(self, p_controller_obj, p_register, p_values)"
},
{
"docstring": "Set the PIM operating mode: Page 6 of UPB Powerline Interface Module (PIM) Description Version 1.6 The PIM mod... | 2 | null | Implement the Python class `CreateCommands` described below.
Class description:
Implement the CreateCommands class.
Method signatures and docstrings:
- def set_register_value(self, p_controller_obj, p_register, p_values): Set one of the device's registers.
- def set_pim_mode(self): Set the PIM operating mode: Page 6 ... | Implement the Python class `CreateCommands` described below.
Class description:
Implement the CreateCommands class.
Method signatures and docstrings:
- def set_register_value(self, p_controller_obj, p_register, p_values): Set one of the device's registers.
- def set_pim_mode(self): Set the PIM operating mode: Page 6 ... | a100fc67761a22ae47ed6f21f3c9464e2de5d54f | <|skeleton|>
class CreateCommands:
def set_register_value(self, p_controller_obj, p_register, p_values):
"""Set one of the device's registers."""
<|body_0|>
def set_pim_mode(self):
"""Set the PIM operating mode: Page 6 of UPB Powerline Interface Module (PIM) Description Version 1.6 The... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class CreateCommands:
def set_register_value(self, p_controller_obj, p_register, p_values):
"""Set one of the device's registers."""
LOG.debug('Setting register {:#0x} to value {}'.format(p_register, p_values))
BuildCommand.write_register_command(p_controller_obj, p_register, p_values)
... | the_stack_v2_python_sparse | Project/src/Modules/House/Family/Upb/upb_pim.py | DBrianKimmel/PyHouse | train | 3 |
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